role of technology in banking essay

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Essay Writing on Role of technology in Banking Sector – RBI Grade B 2023

Write an argrumentative essay on “Role of technology in the banking sector and its impact on customers” for RBI Grade B 2023

The banking sector has undergone a significant transformation in recent years, thanks to the role of technology. Technology has revolutionized the banking industry by making it more efficient, secure, and accessible. This essay argues that the role of technology in the banking sector has had a positive impact on customers.

One of the most significant impacts of technology in the banking sector is the convenience it offers to customers. Customers can now access their bank accounts and conduct transactions from the comfort of their homes or offices, thanks to online and mobile banking. This has eliminated the need for customers to visit their bank branches, which can be time-consuming and inconvenient. Customers can now transfer funds, pay bills, and access account information with ease.

Technology has also made banking transactions more secure. With the implementation of measures such as two-factor authentication and biometric identification, customers can be sure that their transactions are safe and secure. This has reduced the incidence of fraud and made it more difficult for cybercriminals to steal customer information.

The role of technology in the banking sector has also increased the speed and efficiency of transactions. Automated teller machines (ATMs) and online banking have reduced the time it takes for customers to access their funds and conduct transactions. Customers can now withdraw cash, deposit cheques, and transfer funds quickly and easily.

Another significant impact of technology on customers is the access it has provided to banking services. Technology has made it possible for banks to offer their services to customers who previously did not have access to banking services. This has had a positive impact on financial inclusion, especially in developing countries.

In conclusion, the role of technology in the banking sector has had a positive impact on customers. It has made banking more convenient, secure, and accessible. Technology has also increased the speed and efficiency of transactions and contributed to financial inclusion. However, it is important for banks to ensure that they maintain the privacy and security of their customers’ information to ensure that technology continues to have a positive impact on customers.

• Open access
• Published: 18 June 2021

Financial technology and the future of banking

• Daniel Broby   ORCID: orcid.org/0000-0001-5482-0766 1  

Financial Innovation volume  7 , Article number:  47 ( 2021 ) Cite this article

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This paper presents an analytical framework that describes the business model of banks. It draws on the classical theory of banking and the literature on digital transformation. It provides an explanation for existing trends and, by extending the theory of the banking firm, it illustrates how financial intermediation will be impacted by innovative financial technology applications. It further reviews the options that established banks will have to consider in order to mitigate the threat to their profitability. Deposit taking and lending are considered in the context of the challenge made from shadow banking and the all-digital banks. The paper contributes to an understanding of the future of banking, providing a framework for scholarly empirical investigation. In the discussion, four possible strategies are proposed for market participants, (1) customer retention, (2) customer acquisition, (3) banking as a service and (4) social media payment platforms. It is concluded that, in an increasingly digital world, trust will remain at the core of banking. That said, liquidity transformation will still have an important role to play. The nature of banking and financial services, however, will change dramatically.

Introduction

The bank of the future will have several different manifestations. This paper extends theory to explain the impact of financial technology and the Internet on the nature of banking. It provides an analytical framework for academic investigation, highlighting the trends that are shaping scholarly research into these dynamics. To do this, it re-examines the nature of financial intermediation and transactions. It explains how digital banking will be structurally, as well as physically, different from the banks described in the literature to date. It does this by extending the contribution of Klein ( 1971 ), on the theory of the banking firm. It presents suggested strategies for incumbent, and challenger banks, and how banking as a service and social media payment will reshape the competitive landscape.

The banking industry has been evolving since Banca Monte dei Paschi di Siena opened its doors in 1472. Its leveraged business model has proved very scalable over time, but it is now facing new challenges. Firstly, its book to capital ratios, as documented by Berger et al ( 1995 ), have been consistently falling since 1840. This trend continues as competition has increased. In the past decade, the industry has experienced declines in profitability as measured by return on tangible equity. This is partly the result of falling leverage and fee income and partly due to the net interest margin (connected to traditional lending activity). These trends accelerated following the 2008 financial crisis. At the same time, technology has made banks more competitive. Advances in digital technology are changing the very nature of banking. Banks are now distributing services via mobile technology. A prolonged period of very low interest rates is also having an impact. To sustain their profitability, Brei et al. ( 2020 ) note that many banks have increased their emphasis on fee-generating services.

As Fama ( 1980 ) explains, a bank is an intermediary. The Internet is, however, changing the way financial service providers conduct their role. It is fundamentally changing the nature of the banking. This in turn is changing the nature of banking services, and the way those services are delivered. As a consequence, in order to compete in the changing digital landscape, banks have to adapt. The banks of the future, both incumbents and challengers, need to address liquidity transformation, data, trust, competition, and the digitalization of financial services. Against this backdrop, incumbent banks are focused on reinventing themselves. The challenger banks are, however, starting with a blank canvas. The research questions that these dynamics pose need to be investigated within the context of the theory of banking, hence the need to revise the existing analytical framework.

Banks perform payment and transfer functions for an economy. The Internet can now facilitate and even perform these functions. It is changing the way that transactions are recorded on ledgers and is facilitating both public and private digital currencies. In the past, banks operated in a world of information asymmetry between themselves and their borrowers (clients), but this is changing. This differential gave one bank an advantage over another due to its knowledge about its clients. The digital transformation that financial technology brings reduces this advantage, as this information can be digitally analyzed.

Even the nature of deposits is being transformed. Banks in the future will have to accept deposits and process transactions made in digital form, either Central Bank Digital Currencies (CBDC) or cryptocurrencies. This presents a number of issues: (1) it changes the way financial services will be delivered, (2) it requires a discussion on resilience, security and competition in payments, (3) it provides a building block for better cross border money transfers and (4) it raises the question of private and public issuance of money. Braggion et al ( 2018 ) consider whether these represent a threat to financial stability.

The academic study of banking began with Edgeworth ( 1888 ). He postulated that it is based on probability. In this respect, the nature of the business model depends on the probability that a bank will not be called upon to meet all its liabilities at the same time. This allows banks to lend more than they have in deposits. Because of the resultant mismatch between long term assets and short-term liabilities, a bank’s capital structure is very sensitive to liquidity trade-offs. This is explained by Diamond and Rajan ( 2000 ). They explain that this makes a bank a’relationship lender’. In effect, they suggest a bank is an intermediary that has borrowed from other investors.

Diamond and Rajan ( 2000 ) argue a lender can negotiate repayment obligations and that a bank benefits from its knowledge of the customer. As shall be shown, the new generation of digital challenger banks do not have the same tradeoffs or knowledge of the customer. They operate more like a broker providing a platform for banking services. This suggests that there will be more than one type of bank in the future and several different payment protocols. It also suggests that banks will have to data mine customer information to improve their understanding of a client’s financial needs.

The key focus of Diamond and Rajan ( 2000 ), however, was to position a traditional bank is an intermediary. Gurley and Shaw ( 1956 ) describe how the customer relationship means a bank can borrow funds by way of deposits (liabilities) and subsequently use them to lend or invest (assets). In facilitating this mediation, they provide a service whereby they store money and provide a mechanism to transmit money. With improvements in financial technology, however, money can be stored digitally, lenders and investors can source funds directly over the internet, and money transfer can be done digitally.

A review of financial technology and banking literature is provided by Thakor ( 2020 ). He highlights that financial service companies are now being provided by non-deposit taking contenders. This paper addresses one of the four research questions raised by his review, namely how theories of financial intermediation can be modified to accommodate banks, shadow banks, and non-intermediated solutions.

To be a bank, an entity must be authorized to accept retail deposits. A challenger bank is, therefore, still a bank in the traditional sense. It does not, however, have the costs of a branch network. A peer-to-peer lender, meanwhile, does not have a deposit base and therefore acts more like a broker. This leads to the issue that this paper addresses, namely how the banks of the future will conduct their intermediation.

In order to understand what the bank of the future will look like, it is necessary to understand the nature of the aforementioned intermediation, and the way it is changing. In this respect, there are two key types of intermediation. These are (1) quantitative asset transformation and, (2) brokerage. The latter is a common model adopted by challenger banks. Figure  1 depicts how these two types of financial intermediation match savers with borrowers. To avoid nuanced distinction between these two types of intermediation, it is common to classify banks by the services they perform. These can be grouped as either private, investment, or commercial banking. The service sub-groupings include payments, settlements, fund management, trading, treasury management, brokerage, and other agency services.

How banks act as intermediaries between lenders and borrowers. This function call also be conducted by intermediaries as brokers, for example by shadow banks. Disintermediation occurs over the internet where peer-to-peer lenders match savers to lenders

Financial technology has the ability to disintermediate the banking sector. The competitive pressures this results in will shape the banks of the future. The channels that will facilitate this are shown in Fig.  2 , namely the Internet and/or mobile devices. Challengers can participate in this by, (1) directly matching borrows with savers over the Internet and, (2) distributing white labels products. The later enables banking as a service and avoids the aforementioned liquidity mismatch.

The strategic options banks have to match lenders with borrowers. The traditional and challenger banks are in the same space, competing for business. The distributed banks use the traditional and challenger banks to white label banking services. These banks compete with payment platforms on social media. The Internet heralds an era of banking as a service

There are also physical changes that are being made in the delivery of services. Bricks and mortar branches are in decline. Mobile banking, or m-banking as Liu et al ( 2020 ) describe it, is an increasingly important distribution channel. Robotics are increasingly being used to automate customer interaction. As explained by Vishnu et al ( 2017 ), these improve efficiency and the quality of execution. They allow for increased oversight and can be built on legacy systems as well as from a blank canvas. Application programming interfaces (APIs) are bringing the same type of functionality to m-banking. They can be used to authorize third party use of banking data. How banks evolve over time is important because, according to the OECD, the activity in the financial sector represents between 20 and 30 percent of developed countries Gross Domestic Product.

In summary, financial technology has evolved to a level where online banks and banking as a service are challenging incumbents and the nature of banking mediation. Banking is rapidly transforming because of changes in such technology. At the same time, the solving of the double spending problem, whereby digital money can be cryptographically protected, has led to the possibility that paper money will become redundant at some point in the future. A theoretical framework is required to understand this evolving landscape. This is discussed next.

The theory of the banking firm: a revision

In financial theory, as eloquently explained by Fama ( 1980 ), banking provides an accounting system for transactions and a portfolio system for the storage of assets. That will not change for the banks of the future. Fama ( 1980 ) explains that their activities, in an unregulated state, fulfil the Modigliani–Miller ( 1959 ) theorem of the irrelevance of the financing decision. In practice, traditional banks compete for deposits through the interest rate they offer. This makes the transactional element dependent on the resulting debits and credits that they process, essentially making banks into bookkeeping entities fulfilling the intermediation function. Since this is done in response to competitive forces, the general equilibrium is a passive one. As such, the banking business model is vulnerable to disruption, particularly by innovation in financial technology.

A bank is an idiosyncratic corporate entity due to its ability to generate credit by leveraging its balance sheet. That balance sheet has assets on one side and liabilities on the other, like any corporate entity. The assets consist of cash, lending, financial and fixed assets. On the other side of the balance sheet are its liabilities, deposits, and debt. In this respect, a bank’s equity and its liabilities are its source of funds, and its assets are its use of funds. This is explained by Klein ( 1971 ), who notes that a bank’s equity W , borrowed funds and its deposits B is equal to its total funds F . This is the same for incumbents and challengers. This can be depicted algebraically if we let incumbents be represented by Φ and challengers represented by Γ:

Klein ( 1971 ) further explains that a bank’s equity is therefore made up of its share capital and unimpaired reserves. The latter are held by a bank to protect the bank’s deposit clients. This part is also mandated by regulation, so as to protect customers and indeed the entire banking system from systemic failure. These protective measures include other prudential requirements to hold cash reserves or other liquid assets. As shall be shown, banking services can be performed over the Internet without these protections. Banking as a service, as this phenomenon known, is expected to increase in the future. This will change the nature of the protection available to clients. It will change the way banks transform assets, explained next.

A bank’s deposits are said to be a function of the proportion of total funds obtained through the issuance of the ith deposit type and its total funds F , represented by α i . Where deposits, represented by Bs , are made in the form of Bs (i  =  1 *s n) , they generate a rate of interest. It follows that Si Bs  =  B . As such,

Therefor it can be said that,

The importance of Eq. 3 is that the balance sheet can be leveraged by the issuance of loans. It should be noted, however, that not all loans are returned to the bank in whole or part. Non-performing loans reduce the asset side of a bank’s balance sheet and act as a constraint on capital, and therefore new lending. Clearly, this is not the case with banking as a service. In that model, loans are brokered. That said, with the traditional model, an advantage of financial technology is that it facilitates the data mining of clients’ accounts. Lending can therefore be more targeted to borrowers that are more likely to repay, thereby reducing non-performing loans. Pari passu, the incumbent bank of the future will therefore have a higher risk-adjusted return on capital. In practice, however, banking as a service will bring greater competition from challengers and possible further erosion of margins. Alternatively, some banks will proactively engage in partnerships and acquisitions to maintain their customer base and address the competition.

A bank must have reserves to meet the demand of customers demanding their deposits back. The amount of these reserves is a key function of banking regulation. The Basel Committee on Banking Supervision mandates a requirement to hold various tiers of capital, so that banks have sufficient reserves to protect depositors. The Committee also imposes a framework for mitigating excessive liquidity risk and maturity transformation, through a set Liquidity Coverage Ratio and Net Stable Funding Ratio.

Recent revisions of theory, because of financial technology advances, have altered our understanding of banking intermediation. This will impact the competitive landscape and therefor shape the nature of the bank of the future. In this respect, the threat to incumbent banks comes from peer-to-peer Internet lending platforms. These perform the brokerage function of financial intermediation without the use of the aforementioned banking balance sheet. Unlike regulated deposit takers, such lending platforms do not create assets and do not perform risk and asset transformation. That said, they are reliant on investors who do not always behave in a counter cyclical way.

Financial technology in banking is not new. It has been used to facilitate electronic markets since the 1980’s. Thakor ( 2020 ) refers to three waves of application of financial innovation in banking. The advent of institutional futures markets and the changing nature of financial contracts fundamentally changed the role of banks. In response to this, academics extended the concept of a bank into an entity that either fulfills the aforementioned functions of a broker or a qualitative asset transformer. In this respect, they connect the providers and users of capital without changing the nature of the transformation of the various claims to that capital. This transformation can be in the form risk transfer or the application of leverage. The nature of trading of financial assets, however, is changing. Price discovery can now be done over the Internet and that is moving liquidity from central marketplaces (like the stock exchange) to decentralized ones.

Alongside these trends, in considering what the bank of the future will look like, it is necessary to understand the unregulated lending market that competes with traditional banks. In this part of the lending market, there has been a rise in shadow banks. The literature on these entities is covered by Adrian and Ashcraft ( 2016 ). Shadow banks have taken substantial market share from the traditional banks. They fulfil the brokerage function of banks, but regulators have only partial oversight of their risk transformation or leverage. The rise of shadow banks has been facilitated by financial technology and the originate to distribute model documented by Bord and Santos ( 2012 ). They use alternative trading systems that function as electronic communication networks. These facilitate dark pools of liquidity whereby buyers and sellers of bonds and securities trade off-exchange. Since the credit crisis of 2008, total broker dealer assets have diverged from banking assets. This illustrates the changed lending environment.

In the disintermediated market, banking as a service providers must rely on their equity and what access to funding they can attract from their online network. Without this they are unable to drive lending growth. To explain this, let I represent the online network. Extending Klein ( 1971 ), further let Ψ represent banking as a service and their total funds by F . This state is depicted as,

Theoretically, it can be shown that,

Shadow banks, and those disintermediators who bypass the banking system, have an advantage in a world where technology is ubiquitous. This becomes more apparent when costs are considered. Buchak et al. ( 2018 ) point out that shadow banks finance their originations almost entirely through securitization and what they term the originate to distribute business model. Diversifying risk in this way is good for individual banks, as banking risks can be transferred away from traditional banking balance sheets to institutional balance sheets. That said, the rise of securitization has introduced systemic risk into the banking sector.

Thus, we can see that the nature of banking capital is changing and at the same time technology is replacing labor. Let A denote the number of transactions per account at a period in time, and C denote the total cost per account per time period of providing the services of the payment mechanism. Klein ( 1971 ) points out that, if capital and labor are assumed to be part of the traditional banking model, it can be observed that,

It can therefore be observed that the total service charge per account at a period in time, represented by S, has a linear and proportional relationship to bank account activity. This is another variable that financial technology can impact. According to Klein ( 1971 ) this can be summed up in the following way,

where d is the basic bank decision variable, the service charge per transaction. Once again, in an automated and digital environment, financial technology greatly reduces d for the challenger banks. Swankie and Broby ( 2019 ) examine the impact of Artificial Intelligence on the evaluation of banking risk and conclude that it improves such variables.

Meanwhile, the traditional banking model can be expressed as a product of the number of accounts, M , and the average size of an account, N . This suggests a banks implicit yield is it rate of interest on deposits adjusted by its operating loss in each time period. This yield is generated by payment and loan services. Let R 1 depict this. These can be expressed as a fraction of total demand deposits. This is depicted by Klein ( 1971 ), if one assumes activity per account is constant, as,

As a result, whether a bank is structured with traditional labor overheads or built digitally, is extremely relevant to its profitability. The capital and labor of tradition banks, depicted as Φ i , is greater than online networks, depicted as I i . As such, the later have an advantage. This can be shown as,

What Klein (1972) failed to highlight is that the banking inherently involves leverage. Diamond and Dybving (1983) show that leverage makes bank susceptible to run on their liquidity. The literature divides these between adverse shock events, as explained by Bernanke et al ( 1996 ) or moral hazard events as explained by Demirgu¨¸c-Kunt and Detragiache ( 2002 ). This leverage builds on the balance sheet mismatch of short-term assets with long term liabilities. As such, capital and liquidity are intrinsically linked to viability and solvency.

The way capital and liquidity are managed is through credit and default management. This is done at a bank level and a supervisory level. The Basel Committee on Banking Supervision applies capital and leverage ratios, and central banks manage interest rates and other counter-cyclical measures. The various iterations of the prudential regulation of banks have moved the microeconomic theory of banking from the modeling of risk to the modeling of imperfect information. As mentioned, shadow and disintermediated services do not fall under this form or prudential regulation.

The relationship between leverage and insolvency risk crucially depends on the degree of banks total funds F and their liability structure L . In this respect, the liability structure of traditional banks is also greater than online networks which do not have the same level of available funds, depicted as,

Diamond and Dybvig ( 1983 ) observe that this liability structure is intimately tied to a traditional bank’s assets. In this respect, a bank’s ability to finance its lending at low cost and its ability to achieve repayment are key to its avoidance of insolvency. Online networks and/or brokers do not have to finance their lending, simply source it. Similarly, as brokers they do not face capital loss in the event of a default. This disintermediates the bank through the use of a peer-to-peer environment. These lenders and borrowers are introduced in digital way over the internet. Regulators have taken notice and the digital broker advantage might not last forever. As a result, the future may well see greater cooperation between these competing parties. This also because banks have valuable operational experience compared to new entrants.

It should also be observed that bank lending is either secured or unsecured. Interest on an unsecured loan is typically higher than the interest on a secured loan. In this respect, incumbent banks have an advantage as their closeness to the customer allows them to better understand the security of the assets. Berger et al ( 2005 ) further differentiate lending into transaction lending, relationship lending and credit scoring.

The evolution of the business model in a digital world

As has been demonstrated, the bank of the future in its various manifestations will be a consequence of the evolution of the current banking business model. There has been considerable scholarly investigation into the uniqueness of this business model, but less so on its changing nature. Song and Thakor ( 2010 ) are helpful in this respect and suggest that there are three aspects to this evolution, namely competition, complementary and co-evolution. Although liquidity transformation is evolving, it remains central to a bank’s role.

All the dynamics mentioned are relevant to the economy. There is considerable evidence, as outlined by Levine ( 2001 ), that market liberalization has a causal impact on economic growth. The impact of technology on productivity should prove positive and enhance the functioning of the domestic financial system. Indeed, market liberalization has already reshaped banking by increasing competition. New fee based ancillary financial services have become widespread, as has the proprietorial use of balance sheets. Risk has been securitized and even packaged into trade-able products.

Challenger banks are developing in a complementary way with the incumbents. The latter have an advantage over new entrants because they have information on their customers. The liquidity insurance model, proposed by Diamond and Dybvig ( 1983 ), explains how such banks have informational advantages over exchange markets. That said, financial technology changes these dynamics. It if facilitating the processing of financial data by third parties, explained in greater detail in the section on Open Banking.

At the same time, financial technology is facilitating banking as a service. This is where financial services are delivered by a broker over the Internet without resort to the balance sheet. This includes roboadvisory asset management, peer to peer lending, and crowd funding. Its growth will be facilitated by Open Banking as it becomes more geographically adopted. Figure  3 illustrates how these business models are disintermediating the traditional banking role and matching burrowers and savers.

The traditional view of banks ecosystem between savers and borrowers, atop the Internet which is matching savers and borrowers directly in a peer-to-peer way. The Klein ( 1971 ) theory of the banking firm does not incorporate the mirrored dynamics, and as such needs to be extended to reflect the digital innovation that impacts both borrowers and severs in a peer-to-peer environment

Meanwhile, the banking sector is co-evolving alongside a shadow banking phenomenon. Lenders and borrowers are interacting, but outside of the banking sector. This is a concern for central banks and banking regulators, as the lending is taking place in an unregulated environment. Shadow banking has grown because of financial technology, market liberalization and excess liquidity in the asset management ecosystem. Pozsar and Singh ( 2011 ) detail the non-bank/bank intersection of shadow banking. They point out that shadow banking results in reverse maturity transformation. Incumbent banks have blurred the distinction between their use of traditional (M2) liabilities and market-based shadow banking (non-M2) liabilities. This impacts the inter-generational transfers that enable a bank to achieve interest rate smoothing.

Securitization has transformed the risk in the banking sector, transferring it to asset management institutions. These include structured investment vehicles, securities lenders, asset backed commercial paper investors, credit focused hedge and money market funds. This in turn has led to greater systemic risk, the result of the nature of the non-traded liabilities of securitized pooling arrangements. This increased risk manifested itself in the 2008 credit crisis.

Commercial pressures are also shaping the banking industry. The drive for cost efficiency has made incumbent banks address their personally costs. Bank branches have been closed as technology has evolved. Branches make it easier to withdraw or transfer deposits and challenger banks are not as easily able to attract new deposits. The banking sector is therefore looking for new point of customer contact, such as supermarkets, post offices and social media platforms. These structural issues are occurring at the same time as the retail high street is also evolving. Banks have had an aggressive roll out of automated telling machines and a reduction in branches and headcount. Online digital transactions have now become the norm in most developed countries.

The financing of banks is also evolving. Traditional banks have tended to fund illiquid assets with short term and unstable liquid liabilities. This is one of the key contributors to the rise to the credit crisis of 2008. The provision of liquidity as a last resort is central to the asset transformation process. In this respect, the banking sector experienced a shock in 2008 in what is termed the credit crisis. The aforementioned liquidity mismatch resulted in the system not being able to absorb all the risks associated with subprime lending. Central banks had to resort to quantitative easing as a result of the failure of overnight funding mechanisms. The image of the entire banking sector was tarnished, and the banks of the future will have to address this.

The future must learn from the mistakes of the past. The structural weakness of the banking business model cannot be solved. That said, the latest Basel rules introduce further risk mitigation, improved leverage ratios and increased levels of capital reserve. Another lesson of the credit crisis was that there should be greater emphasis on risk culture, governance, and oversight. The independence and performance of the board, the experience and the skill set of senior management are now a greater focus of regulators. Internal controls and data analysis are increasingly more robust and efficient, with a greater focus on a banks stable funding ratio.

Meanwhile, the very nature of money is changing. A digital wallet for crypto-currencies fulfills much the same storage and transmission functions of a bank; and crypto-currencies are increasing being used for payment. Meanwhile, in Sweden, stores have the right to refuse cash and the majority of transactions are card based. This move to credit and debit cards, and the solving of the double spending problem, whereby digital money can be crypto-graphically protected, has led to the possibility that paper money could be replaced at some point in the future. Whether this might be by replacement by a CBDC, or decentralized digital offering, is of secondary importance to the requirement of banks to adapt. Whether accommodating crytpo-currencies or CBDC’s, Kou et al. ( 2021 ) recommend that banks keep focused on alternative payment and money transferring technologies.

Central banks also have to adapt. To limit disintermediation, they have to ensure that the economic design of their sponsored digital currencies focus on access for banks, interest payment relative to bank policy rate, banking holding limits and convertibility with bank deposits. All these developments have implications for banks, particularly in respect of funding, the secure storage of deposits and how digital currency interacts with traditional fiat money.

Open banking

Against the backdrop of all these trends and changes, a new dynamic is shaping the future of the banking sector. This is termed Open Banking, already briefly mentioned. This new way of handling banking data protocols introduces a secure way to give financial service companies consensual access to a bank’s customer financial information. Figure  4 illustrates how this works. Although a fairly simple concept, the implications are important for the banking industry. Essentially, a bank customer gives a regulated API permission to securely access his/her banking website. That is then used by a banking as a service entity to make direct payments and/or download financial data in order to provide a solution. It heralds an era of customer centric banking.

How Open Banking operates. The customer generates data by using his bank account. A third party provider is authorized to access that data through an API request. The bank confirms digitally that the customer has authorized the exchange of data and then fulfills the request

Open Banking was a response to the documented inertia around individual’s willingness to change bank accounts. Following the Retail Banking Review in the UK, this was addressed by lawmakers through the European Union’s Payment Services Directive II. The legislation was designed to make it easier to change banks by allowing customers to delegate authority to transfer their financial data to other parties. As a result of this, a whole host of data centric applications were conceived. Open banking adds further momentum to reshaping the future of banking.

Open Banking has a number of quite revolutionary implications. It was started so customers could change banks easily, but it resulted in some secondary considerations which are going to change the future of banking itself. It gives a clear view of bank financing. It allows aggregation of finances in one place. It also allows can give access to attractive offerings by allowing price comparisons. Open Banking API’s build a secure online financial marketplace based on data. They also allow access to a larger market in a faster way but the third-party providers for the new entrants. Open Banking allows developers to build single solutions on an API addressing very specific problems, like for example, a cash flow based credit rating.

Romānova et al. ( 2018 ) undertook a questionnaire on the Payment Services Directive II. The results suggest that Open Banking will promote competitiveness, innovation, and new product development. The initiative is associated with low costs and customer satisfaction, but that some concerns about security, privacy and risk are present. These can be mitigated, to some extent, by secure protocols and layered permission access.

Discussion: strategic options

Faced with these disruptive trends, there are four strategic options for market participants to con- sider. There are (1) a defensive customer retention strategy for incumbents, (2) an aggressive customer acquisition strategy for challenger banks (3) a banking as a service strategy for new entrants, and (4) a payments strategy for social media platforms.

Each of these strategies has to be conducted in a competitive marketplace for money demand by potential customers. Figure  5 illustrates where the first three strategies lie on the tradeoff between money demand and interest rates. The payment strategy can’t be modeled based on the supply of money. In the figure, the market settles at a rate L 2 . The incumbent banks have the capacity to meet the largest supply of these loans. The challenger banks have a constrained function but due to a lower cost base can gain excess rent through higher rates of interest. The peer-to-peer bank as a service brokers must settle for the market rate and a constrained supply offering.

The money demand M by lenders on the y axis. Interest rates on the y axis are labeled as r I and r II . The challenger banks are represented by the line labeled Γ. They have a price and technology advantage and so can lend at higher interest rates. The brokers are represented by the line labeled Ω. They are price takers, accepting the interest rate determined by the market. The same is true for the incumbents, represented by the line labeled Φ but they have a greater market share due to their customer relationships. Note that payments strategy for social media platforms is not shown on this figure as it is not affected by interest rates

Figure  5 illustrates that having a niche strategy is not counterproductive. Liu et al ( 2020 ) found that banks performing niche activities exhibit higher profitability and have lower risk. The syndication market now means that a bank making a loan does not have to be the entity that services it. This means banks in the future can better shape their risk profile and manage their lending books accordingly.

An interesting question for central banks is what the future Deposit Supply function will look like. If all three forms: open banking, traditional banking and challenger banks develop together, will the bank of the future have the same Deposit Supply function? The Klein ( 1971 ) general formulation assumes that deposits are increasing functions of implicit and explicit yields. As such, the very nature of central bank directed monetary policy may have to be revisited, as alluded to in the earlier discussion on digital money.

The client retention strategy (incumbents)

The competitive pressures suggest that incumbent banks need to focus on customer retention. Reichheld and Kenny ( 1990 ) found that the best way to do this was to focus on the retention of branch deposit customers. Obviously, another way is to provide a unique digital experience that matches the challengers.

Incumbent banks have a competitive advantage based on the information they have about their customers. Allen ( 1990 ) argues that where risk aversion is observable, information markets are viable. In other words, both bank and customer benefit from this. The strategic issue for them, therefore, becomes the retention of these customers when faced with greater competition.

Open Banking changes the dynamics of the banking information advantage. Borgogno and Colangelo ( 2020 ) suggest that the access to account (XS2A) rule that it introduced will increase competition and reduce information asymmetry. XS2A requires banks to grant access to bank account data to authorized third payment service providers.

The incumbent banks have a high-cost base and legacy IT systems. This makes it harder for them to migrate to a digital world. There are, however, also benefits from financial technology for the incumbents. These include reduced cost and greater efficiency. Financial technology can also now support platforms that allow incumbent banks to sell NPL’s. These platforms do not require the ownership of assets, they act as consolidators. The use of technology to monitor the transactions make the processing cost efficient. The unique selling point of such platforms is their centralized point of contact which results in a reduction in information asymmetry.

Incumbent banks must adapt a number of areas they got to adapt in terms of their liquidity transformation. They have to adapt the way they handle data. They must get customers to trust them in a digital world and the way that they trust them in a bricks and mortar world. It is no coincidence. When you go into a bank branch that is a great big solid building great big facade and so forth that is done deliberately so that you trust that bank with your deposit.

The risk of having rising non-performing loans needs to be managed, so customer retention should be selective. One of the puzzles in banking is why customers are regularly denied credit, rather than simply being charged a higher price for it. This credit rationing is often alleviated by collateral, but finance theory suggests value is based on the discounted sum of future cash flows. As such, it is conceivable that the bank of the future will use financial technology to provide innovative credit allocation solutions. That said, the dual risks of moral hazard and information asymmetries from the adoption of such solutions must be addressed.

Customer retention is especially important as bank competition is intensifying, as is the digitalization of financial services. Customer retention requires innovation, and that innovation has been moving at a very fast rate. Until now, banks have traditionally been hesitant about technology. More recently, mergers and acquisitions have increased quite substantially, initiated by a need to address actual or perceived weaknesses in financial technology.

The client acquisition strategy (challengers)

As intermediaries, the challenger banks are the same as incumbent banks, but designed from the outset to be digital. This gives them a cost and efficiency advantage. Anagnostopoulos ( 2018 ) suggests that the difference between challenger and traditional banks is that the former address its customers problems more directly. The challenge for such banks is customer acquisition.

Open Banking is a major advantage to challenger banks as it facilitates the changing of accounts. There is widespread dissatisfaction with many incumbent banks. Open Banking makes it easier to change accounts and also easier to get a transaction history on the client.

Customer acquisition can be improved by building trust in a brand. Historically, a bank was physically built in a very robust manner, hence the heavy architecture and grand banking halls. This was done deliberately to engender a sense of confidence in the deposit taking institution. Pure internet banks are not able to do this. As such, they must employ different strategies to convey stability. To do this, some communicate their sustainability credentials, whilst others use generational values-based advertising. Customer acquisition in a banking context is traditionally done by offering more attractive rates of interest. This is illustrated in Fig.  5 by the intersect of traditional banks with the market rate of interest, depicted where the line Γ crosses L 2 . As a result of the relationship with banking yield, teaser rates and introductory rates are common. A customer acquisition strategy has risks, as consumers with good credit can game different challenger banks by frequently changing accounts.

Most customer acquisition, however, is done based on superior service offering. The functionality of challenger banking accounts is often superior to incumbents, largely because the latter are built on legacy databases that have inter-operability issues. Having an open platform of services is a popular customer acquisition technique. The unrestricted provision of third-party products is viewed more favorably than a restricted range of products.

The banking as a service strategy (new entrants)

Banking from a customer’s perspective is the provision of a service. Customers don’t care about the maturity transformation of banking balance sheets. Banking as a service can be performed without recourse to these balance sheets. Banking products are brokered, mostly by new entrants, to individuals as services that can be subscribed to or paid on a fee basis.

There are a number banking as a service solutions including pre-paid and credit cards, lending and leasing. The banking as a service brokers are effectively those that are aggregating services from others using open banking to enable banking as a service.

The rise of banking as a service needs to be understood as these compete directly with traditional banks. As explained, some of these do this through peer-to-peer lending over the internet, others by matching borrows and sellers, conducting mediation as a loan broker. Such entities do not transform assets and do not have banking licenses. They do not have a branch network and often don not have access to deposits. This means that they have no insurance protection and can be subject to interest rate controls.

The new genre of financial technology, banking as a service provider, conduct financial services transformation without access to central bank liquidity. In a distributed digital asset world, the assets are stored on a distributed ledger rather than a traditional banking ledger. Financial technology has automated credit evaluation, savings, investments, insurance, trading, banking payments and risk management. These banking as a service offering are only as secure as the technology on which they are built.

The social media payment strategy (disintermediators and disruptors)

An intermediation bank is a conceptual idea, one created solely on a social networking site. Social media has developed a market for online goods and services. Williams ( 2018 ) estimates that there are 2.46 billion social media users. These all make and receive payments of some kind. They demand security and functionality. Importantly, they have often more clients than most banks. As such, a strategy to monetize the payments infrastructure makes sense.

All social media platforms are rich repositories of data. Such platforms are used to buy and sell things and that requires payments. Some platforms are considering evolving their own digital payment, cutting out the banks as middlemen. These include Facebook’s Diem (formerly Libra), a digital currency, and similar developments at some of the biggest technology companies. The risk with social media payment platform is that there is systemic counter-party protection. Regulators need to address this. One way to do this would be to extend payment service insurance to such platforms.

Social media as a platform moves the payment relationship from a transaction to a customer experience. The ability to use consumer desires in combination with financial data has the potential to deliver a number of new revenue opportunities. These will compete directly with the banks of the future. This will have implications for (1) the money supply, (2) the market share of traditional banks and, (3) the services that payment providers offer.

Further research

Several recommendations for research derive from both the impact of disintermediation and the four proposed strategies that will shape banking in the future. The recommendations and suggestions are based on the mentioned papers and the conclusions drawn from them.

As discussed, the nature of intermediation is changing, and this has implications for the pricing of risk. The role of interest rates in banking will have to be further reviewed. In a decentralized world based on crypto currencies the central banks do not have the same control over the money supply, This suggest the quantity theory of money and the liquidity preference theory need to be revisited. As explained, the Internet reduces much of the friction costs of intermediation. Researchers should ask how this will impact maturity transformation. It is also fair to ask whether at some point in the future there will just be one big bank. This question has already been addressed in the literature but the Internet facilities the possibility. Diamond ( 1984 ) and Ramakrishnan and Thakor ( 1984 ) suggested the answer was due to diversification and its impact on reducing monitoring costs.

Attention should be given by academics to the changing nature of banking risk. How should regulators, for example, address the moral hazard posed by challenger banks with weak balance sheets? What about deposit insurance? Should it be priced to include unregulated entities? Also, what criteria do borrowers use to choose non-banking intermediaries? The changing risk environment also poses two interesting practical questions. What will an online bank run look like, and how can it be averted? How can you establish trust in digital services?

There are also research questions related to the nature of competition. What, for example, will be the nature of cross border competition in a decentralized world? Is the credit rationing that generates competition a static or dynamic phenomena online? What is the value of combining consumer utility with banking services?

Financial intermediaries, like banks, thrive in a world of deficits and surpluses supported by information asymmetries and disconnectedness. The connectivity of the internet changes this dynamic. In this respect, the view of Schumpeter ( 1911 ) on the role of financial intermediaries needs revisiting. Lenders and borrows can be connected peer to peer via the internet.

All the dynamics mentioned change the nature of moral hazard. This needs further investigation. There has been much scholarly research on the intrinsic riskiness of the mismatch between banking assets and liabilities. This mismatch not only results in potential insolvency for a single bank but potentially for the whole system. There has, for example, been much debate on the whether a bank can be too big to fail. As a result of the riskiness of the banking model, the banks of the future will be just a liable to fail as the banks of the past.

This paper presented a revision of the theory of banking in a digital world. In this respect, it built on the work of Klein ( 1971 ). It provided an overview of the changing nature of banking intermediation, a result of the Internet and new digital business models. It presented the traditional academic view of banking and how it is evolving. It showed how this is adapted to explain digital driven disintermediation.

It was shown that the banking industry is facing several documented challenges. Risk is being taken of balance sheet, securitized, and brokered. Financial technology is digitalizing service delivery. At the same time, the very nature of intermediation is being changed due to digital currency. It is argued that the bank of the future not only has to face these competitive issues, but that technology will enhance the delivery of banking services and reduce the cost of their delivery.

The paper further presented the importance of the Open Banking revolution and how that facilitates banking as a service. Open Banking is increasing client churn and driving banking as a service. That in turn is changing the way products are delivered.

Four strategies were proposed to navigate the evolving competitive landscape. These are for incumbents to address customer retention; for challengers to peruse a low-cost digital experience; for niche players to provide banking as a service; and for social media platforms to develop payment platforms. In all these scenarios, the banks of the future will have to have digital strategies for both payments and service delivery.

It was shown that both incumbents and challengers are dependent on capital availability and borrowers credit concerns. Nothing has changed in that respect. The risks remain credit and default risk. What is clear, however, is the bank has become intrinsically linked with technology. The Internet is changing the nature of mediation. It is allowing peer to peer matching of borrowers and savers. It is facilitating new payment protocols and digital currencies. Banks need to evolve and adapt to accommodate these. Most of these questions are empirical in nature. The aim of this paper, however, was to demonstrate that an understanding of the banking model is a prerequisite to understanding how to address these and how to develop hypotheses connected with them.

In conclusion, financial technology is changing the future of banking and the way banks intermediate. It is facilitating digital money and the online transmission of financial assets. It is making banks more customer enteric and more competitive. Scholarly investigation into banking has to adapt. That said, whatever the future, trust will remain at the core of banking. Similarly, deposits and lending will continue to attract regulatory oversight.

Availability of data and materials

Diagrams are my own and the code to reproduce them is available in the supplied Latex files.

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Utilization of artificial intelligence in the banking sector: a systematic literature review

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• Omar H. Fares   ORCID: orcid.org/0000-0003-0950-0661 1 ,
• Irfan Butt 1 &
• Seung Hwan Mark Lee 1  

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This study provides a holistic and systematic review of the literature on the utilization of artificial intelligence (AI) in the banking sector since 2005. In this study, the authors examined 44 articles through a systematic literature review approach and conducted a thematic and content analysis on them. This review identifies research themes demonstrating the utilization of AI in banking, develops and classifies sub-themes of past research, and uses thematic findings coupled with prior research to propose an AI banking service framework that bridges the gap between academic research and industry knowledge. The findings demonstrate how the literature on AI and banking extends to three key areas of research: Strategy, Process, and Customer. These findings may benefit marketers and decision-makers in the banking sector to formulate strategic decisions regarding the utilization and optimization of value from AI technologies in the banking sector. This study also provides opportunities for future research.

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Introduction

Digital innovations in the modern banking landscape are no longer discretionary for financial institutions; instead, they are becoming necessary for financial institutions to cope with an increasingly competitive market and changing customer expectations (De Oliveira Santini, 2018 ; Eren, 2021 ; Hua et al., 2019 ; Rajaobelina and Ricard, 2021 ; Valsamidis et al., 2020 ; Yang, 2009 ). In the era of modern banking, many new digital technologies have been driven by artificial intelligence (AI) as the key engine (Dobrescu and Dobrescu, 2018 ), leading to innovative disruptions of banking channels (e.g., automated teller machines, online banking, mobile banking), services (e.g., imaging of checks, voice recognition, chatbots), and solutions (e.g., AI investment advisors and AI credit selectors).

The application of AI in banking is across the board, with uses in the front office (voice assistants and biometrics), middle office (anti-fraud risk monitoring and complex legal and compliance workflows), and back office (credit underwriting with smart contracts infrastructure). Banks are expected to save $447 billion by 2023, by employing AI applications. Almost 80% of the banks in the USA are cognizant of the potential benefits offered by AI (Digalaki, 2022 ). Indeed, the emergence of AI has generated a wealth of opportunities and challenges (Malali and Gopalakrishnan, 2020 ). In the banking context, the use of AI has led to more seamless sales and has guided the development of effective customer relationship management systems (Tarafdar et al., 2019 ). While the focus in the past was on the automation of credit scoring, analyses, and the grants process (Mehrotra, 2019 ), capabilities evolved to support internal systems and processes as well (Caron, 2019 ).

The term AI was first used in 1956 by John McCarthy (McCarthy et al., 1956 ); it refers to systems that act and think like humans in a rational way (Kok et al., 2009 ). In the aftermath of the dot com bubble in 2000, the field of AI shifted toward Web 2.0. era in 2005, and the growth of data and availability of information encouraged more research in AI and its potential (Larson, 2021 ). More recently, technological advancements have opened the doors for AI to facilitate enterprise cognitive computing, which involves embedding algorithms into applications to support organizational processes (Tarafdar et al., 2019 ). This includes improving the speed of information analysis, obtaining more accurate and reliable data outputs, and allowing employees to perform high-level tasks. In recent years, AI-based technologies have been shown to be effective and practical. However, many corporate executives still lack knowledge regarding the strategic utilization of AI in their organizations. For instance, Ransbotham et al. ( 2017 ) found that 85% of business executives viewed AI as a key tool for providing businesses with a sustainable competitive advantage; however, only 39% had a strategic plan for the use of AI, due to the lack of knowledge regarding implementation of AI for their organizations.

Here, we systematically analyze the past and current state of AI and banking literature to understand how it has been utilized within the banking sector historically, propose a service framework, and provide clear future research opportunities. In the past, a limited number of systematic literature reviews have studied AI within the management discipline (e.g., Bavaresco et al., 2020 ; Borges et al., 2020 ; Loureiro et al., 2020 ; Verma et al., 2021 ). However, the current literature lacks either research scope and depth, and/or industry focus. In response, we seek to differentiate our study from prior reviews by providing a specific focus on the banking sector and a more comprehensive analysis involving multiple modes of analysis.

In light of this, we aim to address the following research questions:

What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?

How does AI impact the customer's journey process in the banking sector, from customer acquisition to service delivery?

What are the current research deficits and future directions of research in this field?

Methodology

Selection of articles.

Adhering to the best practices for conducting a Systematic Literature Review (SLR) (see Khan et al., 2003 ; Tranfield et al, 2003 ; Xiao and Watson, 2019 ), we began by selecting the appropriate database and identifying keywords, based on an in-depth review of the literature. Research papers were extracted from Web of Science (WoS) and Scopus. These databases were selected to complement one another and provide access to scholarly articles (Mongeon and Paul-Hus, 2016 ); this was also the first step in ensuring the inclusion of high-quality articles (Harzing and Alakangas, 2016 ). The following query was used to search the title, abstract, and keywords: “Artificial intelligence OR machine learning OR deep learning OR neural networks OR Intelligent systems AND Bank AND consumer OR customer OR user.” The keywords were selected, based on prior literature review, with the goal of covering various business functions, especially focusing on the banking sector (Loureiro et al., 2020 ; Verma et al., 2021 ; Borges et al., 2020 ; Bavaresco et al., 2020 ). The initial search criteria yielded 11,684 papers. These papers were then filtered by “English,” “article only” publications, and using the subject area filter of “Management, Business Finance, accounting and Business,” which resulted in 626 papers.

In this study, we used the preferred reporting method for systematic reviews and meta-analyses (PRISMA) to ensure that we follow the systematic approach and track the flow of data across different stages of the SLR (Moher et al., 2009 ). After extracting the articles, each of the 626 papers was given a distinctive ID number to help differentiate the papers; the ID number was maintained throughout the analysis process. The data were then organized using the following columns: “ID number,” “database source,” “Author,” “title,” “Abstract,” “keywords,” “Year,” Australian Business Deans Council (ABDC) Journals, “and keyword validation columns.”

The exclusion of papers was done systematically in the following manner: a) All duplicate papers in the database were eliminated (105 duplicates); b) as a second quality check, papers not published in ABDC journals (163 papers) were omitted to ensure a quality standard for inclusion in the review,Query a practice consistent with other recent SLRs (Goyal and Kumar, 2021 ; Nusair et al., 2019 ; Pahlevan-Sharif et al., 2019 ); c) in order to ensure the relevance of articles included, and following our research objectives, we excluded non-consumer-related papers, searching for consumers (consumer, customer, user) in the title, abstract, and keywords; this resulted in the removal of 314 papers; d) for the remaining 48 papers, a relevance check was manually conducted to determine whether the papers were indeed related to AI and banking. Papers that specifically focused on the technical computational process of AI were removed (4 papers). This process resulted in the selection of 44 articles for subsequent analyses.

Thematic analysis

A thematic analysis classifies the topics and subtopics being researched. It is a method for identifying, analyzing, and reporting patterns within data (Boyatzis, 1998 ). We followed Chatha and Butt ( 2015 ) to classify the articles into themes and sub-themes using manual coding. Second, we employed the Leximancer software to supplement the manual classification process. The use of these two approaches provides additional validity and quality to the research findings.

Leximancer is a text-mining software that provides conceptual and relational information by identifying concept occurrences and co-occurrences (Leximancer, 2019 ). After uploading all the 44 papers onto Leximancer, we added “English” to the stoplist, which removed words such as “or/and/like” that are not relevant to developing themes. We manually removed irrelevant filler words, such as “pp.,” “Figure,” and “re.” Finally, our results consisted of two maps: a) a conceptual map wherein central themes and concepts are identified, and b) a relational cloud map where a network of connections and relationships are drawn among concepts.

Thematic map

RQ 1: What are the themes and sub-themes that emerge from prior literature regarding the utilization of AI in the banking industry?

We began with a deductive approach to categorize articles into predetermined themes for the theme identification process. We then employed an inductive approach to identify the sub-themes and provide context for the primary themes (See Fig. 1 ). The procedure for determining the primary themes included, a) reviewing previous related systematic literature reviews (Bavaresco et al., 2020 ; Borges et al., 2020 ; Loureiro et al., 2020 ; Verma et al., 2021 ), b) identifying keywords and developing codes (themes) from selected papers; and c) reviewing titles, abstracts, and full papers, if needed, to identify appropriate allocation within these themes. Three primary themes were curated from the process: Strategy, Processes, and Customers (see Fig.  2 ).

Themes by timeline

In the Strategy theme (21 papers), early research shows the potential uses and adoption of AI from an organizational perspective (e.g., Akkoç, 2012 ; Olson et al., 2012 ; Smeureanu et al., 2013 ). Data mining (an essential part of AI) has been used to predict bankruptcy (Olson et al., 2012 ) and to optimize risk models (Akkoç, 2012 ). The increasing use of AI-driven tools to drive organizational effectiveness creates greater business efficiency opportunities for financial institutions, as compared to traditional modes of strategizing and risk model development. The sub-theme Organizational use of AI (14 papers) covers a range of current activities wherein banks use AI to drive organizational value. These organizational uses include the use of AI to drive business strategies and internal business activities. Medhi and Mondal ( 2016 ) highlighted the use of an AI-driven model to predict outsourcing success. Our findings indicate the effectiveness of AI tools in driving efficient organizational strategies; however, there remain several challenges in implementing AI technologies, including the human resources aspect and the organizational culture to allow for such efficiencies (Fountain et al., 2019 ). More recently, there has been a noticeable focus on discussing some of the challenges associated with AI implementation in banking institutions (e.g., Jakšič and Marinč, 2019 ; Mohapatra, 2020 ). The sub-theme Challenges with AI (three papers) covers a range of challenges that organizations face, including the integration of AI in their organizations. Mohapatra ( 2020 ) characterizes some of the key challenges related to human–machine interactions to allow for the sustainable implementation of AI in banking. While much of the current research has focused on technology, our findings indicate that one of the main areas of opportunity in the future is related to adoption and integration. The sub-theme AI and adoption in financial institutions (six papers) covered a range of topics regarding motivation, and barriers to the adoption of AI technology from an organizational standpoint. Fountain et al. ( 2019 ) conceptually highlighted some barriers to organizational adoption, including workers’ fear, company culture, and budget constraints. Overall, in the Strategy theme, organizational uses of AI seemed to be the most prominent, which highlights the consistent focus on technology development compared with technology implementation. However, the literature remains limited in terms of discussions related to the organizational challenges associated with AI implementation.

In the Processes theme (34 papers), after the dot com bubble and with the emergence of Web 2.0, research on AI in the banking sector started to emerge. This could have been triggered by the suggested use of AI to predict stock market movements and stock selection (Kim and Lee, 2004 ; Tseng, 2003 ). At this stage, the literature on AI in the banking sector was related to its use in credit and loan analysis (Baesens et al., 2005 ; Ince and Aktan, 2009 ; Kao et al., 2012 ; Khandani et al., 2010 ). In the early stages of AI implementation, it is essential to develop fast and reliable AI infrastructure (Larson, 2021 ). Baesens et al. ( 2005 ) utilized a neural network approach to better predict loan defaults and early repayments. Ince and Aktan ( 2009 ) used a data mining technique to analyze credit scores and found that the AI-driven data mining approach was more effective than traditional methods. Similarly, Khandani et al. ( 2010 ) found machine-learning-driven models to be effective in analyzing consumer credit risk. The sub-theme, AI and credit (15 papers), covers the use of AI technology, such as machine learning and data mining, to improve credit scoring, analysis, and granting processes. For instance, Alborzi and Khanbabaei ( 2016 ) examined the use of data mining neural network techniques to develop a customer credit scoring model. Post-2013, there has been a noticeable increase in investigating how AI improves processes that go beyond credit analysis. The sub-theme AI and services (20 papers) covers the uses of AI for process improvement and enhancement. These process-related uses of technology include institutional uses of technology to improve internal service processes. For example, Soltani et al. ( 2019 ) examined the use of machine learning to optimize appointment scheduling time, and reduce service time. Overall, regarding the process theme, our findings highlight the usefulness of AI in improving banking processes; however, there remains a gap in practical research regarding the applied integration of technology in the banking system. In addition, while there is an abundance of research on credit risk, the exploration of other financial products remains limited.

In the Customer theme (26 papers), we uncovered the increasing use of AI as a methodological tool to better understand customer adoption of digital banking services. The sub-theme AI and Customer adoption (11 papers) covers the use of AI as a methodological tool to investigate customers’ adoption of digital banking technologies, including both barriers and motivational factors. For example, Arif et al. ( 2020 ) used a neural network approach to investigate barriers to internet-banking adoption by customers. Belanche et al. ( 2019 ) investigate factors related to AI-driven technology adoption in the banking sector. Payne et al. ( 2018 ) examine the drivers of the usage of AI-enabled mobile banking services. In addition, bank marketers have found an opportunity to use AI to better segment, target, and position their banking products and services. The sub-theme, AI and marketing (nine papers), covers the use of AI for different marketing activities, including customer segmentation, development of marketing models, and delivery of more effective marketing campaigns. For example, Smeureanu et al. ( 2013 ) proposed a machine learning technique to segment banking customers. Schwartz et al. ( 2017 ) utilized an AI-based method to examine the resource allocation in targeted advertisements. In recent years, there has been a noticeable trend in investigating how AI shapes customer experience (Soltani et al., 2019 ; Trivedi, 2019 ). The sub-theme of AI and customer experience (Papers 11) covers the use of AI to enhance banking experience and services for customers. For example, Trivedi ( 2019 ) investigated the use of chatbots in banking and their impact on customer experience.

Table 1 highlights the number of papers included in the themes and sub-themes. Overall, the papers related to Processes (77%) were the most frequently occurring, followed by Customer (59%) and Strategy-based (48%) papers. From 2013 onward, there was an increase in the inter-relation between all three areas of Strategy, Processes, and Customers. Since 2016, there has been a surge in research linking the themes of Processes and Customers. More recently, since 2017, papers combining Customers with Strategy have become more frequent.

Leximancer analysis

A Leximancer analysis was conducted on all the papers included in the study. This resulted in two major classifications and 56 distinct concepts. Here, a “concept” refers to a combination of closely related words. When referring to “concept co-occurrence,” we refer to the total number of times two concepts appear together. In comparison, the word association percentage refers to the conditional probability that two concepts will appear side-by-side.

Conceptual and relational analyses

Conceptual analysis refers to the analysis of data based on word frequency and word occurrence, whereas relational analysis refers to the analysis that draws connections between concepts and captures the co-occurrences between words (Leximancer, 2019 ). As Fig.  3 shows, the most prominent concept is “customer,” which provides additional credence to our customer theme. The concept “customer” appeared 2,231 times across all papers. For the concept “customer,” some of the key concept associations include satisfaction (324 co-occurrences and 64% word association), service (185 co-occurrences and 43% word association), and marketing (86 co-occurrences and 42% word association). This may imply the importance of utilizing AI in improving customer service and satisfaction, and in marketing to retain and grow the customer base. For instance, Trivedi ( 2019 ) examined the factors affecting chatbot satisfaction and found that information, system, and service quality, all have a significant positive association with it. Ekinci et al. ( 2014 ) proposed a customer lifetime value model, supported by a deep learning approach, to highlight key indicators in the banking sector. Xu et al. ( 2020 ) examined the effects of AI versus human customer service, and found that customers are more likely to use AI for low-complexity tasks, whereas a human agent is preferred for high-complexity tasks. It is worth noting that most of the research related to the customer theme has utilized a quantitative approach, with limited qualitative papers (i.e., four papers) in recent years.

Concept map of content of all papers included in the study

Not surprisingly, the second most prominent concept is “banking,” which is expected as it is the sector that we are examining. The concept “banking” appeared 1,033 times across all the papers. In the “banking” concept, some of the key concept associations include mobile (248 co-occurrences and 88% word association), internet (152 co-occurrences and 82% word association), adoption (220 co-occurrences and 50% word association), and acceptance (71 co-occurrences and 42% word association). This implies the importance of utilizing AI in mobile- and internet-banking research, along with inquiries related to the adoption and acceptance of AI for such uses. Belanche et al. ( 2019 ) proposed a research framework to provide a deeper understanding of the factors driving AI-driven technology adoption in the banking sector. Payne et al. ( 2018 ) examined digital natives' comfort and attitudes toward AI-enabled mobile banking activities and found that the need for services, attitude toward AI, relative advantage, and trust had a significant positive association with the usage of AI-enabled mobile banking services.

Figure  4 highlights the concept associations and draws connections between concepts. The identification and classification of themes and sub-themes using the deductive method in thematic analysis, and the automated approach using Leximancer, provide a reliable and detailed overview of the prior literature.

Cloud map of content of all papers included in the study

Customer credit solution application-service blueprint

RQ 2: How does AI impact the banking customer’s journey?

A service blueprint is a method that conceptualizes the customer journey while providing a framework for the front/back-end and support processes (Shostack, 1982 ). For a service blueprint to be effective, the core focus should be on the customer, and steps should be developed based on data and expertise (Bitner et al., 2008 ). As previously discussed, one of the key research areas, AI and banking, relates to credit applications and granting decisions; these are processes that directly impact customer accessibility and acquisition. Here, we develop and propose a Customer Credit Solution Application-Service Blueprint (CCSA) based on our earlier analyses.

Not only was the proposed design developed but the future research direction was also extracted from the articles included in this study. We also validated the framework through direct consultation with banking industry professionals. The CCSA model allows marketers, researchers, and banking professionals to gain a deeper understanding of the customer journey, understand the role of AI, provide an overview of future research directions, and highlight the potential for future growth in this field. As seen in Fig.  5 , we divided the service blueprint into four distinct segments: customer journey, front-stage, back-stage, and support processes. The customer journey is the first step in building a customer-centric blueprint, wherein we highlight the steps taken by customers to apply for a credit solution. The front-stage refers to how the customer interacts with a banking touchpoint (e.g., chatbots). Back-stage actions provide support to customer-facing front-stage actions. Support processes aid in internal organizational interactions and back-stage actions. This section lays out the steps for applying for credit solutions online and showcases the integration and use of AI in the process, with examples from the literature.

Customer credit solution application journey

Acquire customer

We begin from the initial step of customer acquisition, and proceed to credit decision, and post-decision (Broby, 2021 ). In the acquisition step, customers are targeted with the goal of landing them on the website and converting them to active customers. The front-stage includes targeted ads , where customers are exposed to ads that are tailored for them. For instance, Schwartz et al. ( 2017 ) utilized a multi-armed bandit approach for a large retail bank to improve customer acquisition, and proposed a method that allows bank marketers to maintain the balance between learning from advertisement data and optimizing advertisement investment. At this stage, the support processes focus on integrating AI as a methodological tool to better understand customers' banking adoption behaviors, in combination with utilizing machine learning to evaluate and update segmentation activities. The building block at this stage, is understanding the factors of online adoption. Sharma et al. ( 2017 ) used the neural network approach to investigate the factors influencing mobile banking adoption. Payne et al. ( 2018 ) examined digital natives' comfort and attitudes toward AI-enabled mobile banking activities. Markinos and Daskalaki ( 2017 ) used machine learning to classify bank customers based on their behavior toward advertisements.

Visit bank’s website & apply for a credit solution

At this stage, banking institutions aim to convert website traffic to credit solution applicants. The integration of robo-advisors will help customers select a credit solution that they can best qualify for, and which meets their banking needs. The availability of a robo-advisor can enhance the service offering, as it can help customers with the appropriate solution after gathering basic personal financial data and validating it instantly with credit reporting agencies. Trivedi ( 2019 ) found that information, system, and service quality are key to ensuring a seamless customer experience with the chatbot, with personalization moderating the constructs. Robo-advisors have task-oriented features (e.g., checking bank accounts) coupled with problem-solving features (e.g., processing credit applications). Following this, the data collected will be consistently examined through the use of machine learning to improve the offering and enhance customer experience. Jagtiani and Lemieux ( 2019 ) used machine learning to optimize data collected through different channels, which helps arrive at appropriate and inclusive credit recommendations. It is important to note that while the proposed process provides immense value to customers and banking institutions, many customers are hesitant to share their information; thus, trust in the banking institution is key to enhancing customer experience.

Receive a decision

After the data have been collected through the online channel, data mining and machine learning will aid in the analysis and provide optimal credit decisions. At this stage, the customer receives a credit decision through the robo-advisor. The traditional approaches for credit decisions usually take up to two weeks, as the application goes to the advisory network, then to the underwriting stage, and finally back to the customer. However, with the integration of AI, the customer can save time and be better informed by receiving an instant credit decision, allowing an increased sense of empowerment and control. The process of arriving at such decisions should provide a balance between managing organizational risk, maximizing profit, and increasing financial inclusion. For instance, Khandani et al. ( 2010 ) utilized machine learning techniques to build a model predicting customers' credit risk. Koutanaei et al. ( 2015 ) proposed a data mining model to provide more confidence in credit scoring systems. From an organizational risk standpoint, Mall ( 2018 ) used a neural network approach to examine the behavior of defaulting customers, so as to minimize credit risk, and increase profitability for credit-providing institutions.

Customer contact call center

At this stage, we outline the relationship between humans and AI. As Xu et al. ( 2020 ) found that customers prefer humans for high-complexity tasks, the integration of human employees for cases that require manual review is vital, as AI can make errors or misevaluate one of the C's of credit (Baiden, 2011 ). While AI provides a wealth of benefits for customers and organizations, we refer to Jakšič and Marinč's ( 2019 ) discussion that relationship banking still plays a key role in providing a competitive advantage for financial institutions. The integration of AI at this stage can be achieved by optimizing banking channels. For instance, banking institutions can optimize appointment scheduling time and reduce service time through the use of machine learning, as proposed by Soltani et al. ( 2019 ).

General discussion

Researchers have recognized the viable use of AI to provide enhanced customer service. As discussed in the CCSA service advice, facilities, such as robo-advisors, can aid in product selection, application for banking solutions, and time-saving in low-complexity tasks. As AI has been shown to be an effective tool for automating banking processes, improving customer satisfaction, and increasing profitability, the field has further evolved to examine issues pertaining to strategic insights. Recent research has been focused on investigating the use of AI to drive business strategies. For instance, researchers have examined the use of AI to simplify internal audit reports and evaluate strategic initiatives (Jindal, 2020 ; Muñoz-Izquierdo et al., 2019 ). The latest research also highlights the challenges associated with AI, whether from the perspective of implementation, culture, or organizational resistance (Fountain et al., 2019 ). Moreover, one of the key challenges uncovered in the CCSA is privacy and security concerns of customers in sharing their information. As AI technologies continue to grow in the banking sector, the privacy-personalization paradox has become a key research area that needs to be examined.

In addition, the COVID-19 pandemic has brought on a plethora of challenges in the implementation of AI in the banking sector. Although banks' interest in AI technologies remains high, the reduction in revenue has resulted in a decrease in short-term investment in AI technologies (Anderson et al., 2021 ). Wu and Olson ( 2020 ) highlight the need for banking institutions to continue investing in AI technologies to reduce future risks and enhance the integration between online and offline channels. From a customer perspective, COVID-19 has led to an uptick in the adoption of AI-driven services such as chatbots, E-KYC (Know your client), and robo-advisors (Agarwal et al., 2022 ).

Future research directions

RQ 3: What are the current research deficits and the future directions of research in this field?

Tables 2 , 3 , and 4 provide a complete list of recommendations for future research. These recommendations were developed by reviewing all the future research directions included in the 44 papers. We followed Watkins' ( 2017 ) rigorous and accelerated data reduction (RADaR) technique, which allows for an effective and systematic way to analyze and synthesize calls for future research (Watkins, 2017 ).

Regarding strategy, as AI continues to grow in the banking industry, financial institutions need to examine how internal stakeholders perceive the value of embracing AI, the role of leadership, and multiple other variables that impact the organizational adoption of AI. Therefore, we recommend that future research investigate the different factors (e.g., leadership role) that impact the organizational adoption of AI technologies. In addition, as more organizations use and accept AI, internal challenges emerge (Jöhnk et al., 2021 ). Thus, we recommend examining the different organizational challenges (e.g., organizational culture) associated with AI adoption.

Regarding processes, AI and credit is one of the areas that has been extensively explored since 2005 (Bhatore et al., 2020 ). We recommend expanding beyond the currently proposed models and challenging the underlying assumptions by exploring new aspects of risks presented with the introduction of AI technologies. In addition, we recommend the use of more practical case studies to validate new and existing models. Additionally, the growth of AI has evoked further exploration of how internal processes can be improved (Akerkar, 2019 ). For instance, we suggest investigating AI-driven models with other financial products/solutions (e.g., investments, deposit accounts, etc.).

Regarding customers, the key theories mentioned in the research papers included in the study are the Technology Acceptance Model (TAM) and diffusion of innovation theories (Anouze and Alamro, 2019 ; Azad, 2016 ; Belanche et al., 2019 ; Payne et al., 2018 ; Sharma et al., 2015 , 2017 ). However, as customers continue to become accustomed to AI, it may be imperative to develop theories that go beyond its acceptance and adoption. Thus, we recommend investigating different variables (e.g., social influence and user trends) and methods (e.g., cross-cultural studies) that impact customers' relationship with AI. The gradual shift toward its customer-centric utilization has prompted the exploration of new dimensions of AI that influence customer experience. Going forward, it is important to understand the impact of AI on customers and how it can be used to improve customer experience.

Limitations and implications

This study had several limitations. During our inclusion/exclusion criteria, it is plausible that some AI/banking papers may have been missed because of the specific keywords used to curate our dataset. In addition, articles may have been missed due to the time when the data were collected, such as Manrai and Gupta ( 2022 ), who examined investors' perceptions of robo-advisors. Second, regarding theme identification, there may be a potential bias toward selecting themes, which may lead to misclassification. In addition, we acknowledge that the papers were extracted only from the WoS and Scopus databases, which may limit our access to certain peer-reviewed outlets.

This research provides insights for practitioners and marketers in the North American banking sector. To assist in the implementation of AI-based decision-making, we encourage banking professionals to consider further refining their use of AI in the credit scoring, analysis, and granting processes to minimize risk, reduce costs, and improve customer experience. However, in doing so, we recommend using AI not only to improve internal processes but also as a tool (e.g., chatbots) to improve customer service for low-complexity tasks, thereby directing employees' efforts to other business-impacting activities. Moreover, we recommend using AI as a marketing segmentation tool to target customers for optimal solutions.

This study systematically reviewed the literature (44 papers) on AI and banking from 2005 to 2020. We believe that our findings may benefit industry professionals and decision-makers in formulating strategic decisions regarding the different uses of AI in the banking sector, and optimizing the value derived from AI technologies. We advance the field by providing a more comprehensive outlook specific to the area of AI and banking, reflecting the history and future opportunities for AI in shaping business strategies, improving logistics processes, and enhancing customer value.

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Omar H. Fares, Irfan Butt & Seung Hwan Mark Lee

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Fares, O.H., Butt, I. & Lee, S.H.M. Utilization of artificial intelligence in the banking sector: a systematic literature review. J Financ Serv Mark 28 , 835–852 (2023). https://doi.org/10.1057/s41264-022-00176-7

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DOI : https://doi.org/10.1057/s41264-022-00176-7

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This paper analyzes the importance of information technology (IT) in banking for entrepreneurship. To guide our empirical analysis, we build a parsimonious model of bank screening and lending that predicts that IT in banking can spur entrepreneurship by making it easier for startups to borrow against collateral. We provide empirical evidence that job creation by young firms is stronger in US counties that are more exposed to ITintensive banks. Consistent with a strengthened collateral lending channel for IT banks, entrepreneurship increases more in IT-exposed counties when house prices rise. In line with the model's implications, IT in banking increases startup activity without diminishing startup quality and it also weakens the importance of geographical distance between borrowers and lenders. These results suggest that banks' IT adoption can increase dynamism and productivity.

• 1 Introduction

The rise of information technology (IT) in the financial sector has dramatically changed how information is gathered, processed, and analyzed ( Liberti and Petersen, 2017 ). This development may have important implications for banks’ credit supply, as one of their key function is to screen and monitor borrowers. Financing for opaque borrowers, such as young firms that have produced limited hard information, is likely to be especially sensitive to such changes in lenders’ technology. In light of the disproportionate contribution of startups to job creation and productivity growth ( Haltiwanger, Jarmin and Miranda, 2013 ; Klenow and Li, 2020 ) and of the importance of bank credit for new firms, 1 understanding how the IT revolution in banking has affected startup access to finance is of paramount importance. Yet, direct evidence on the impact of lenders’ IT capability on firm formation is scarce.

This paper analyzes how the rise of IT in the financial sector affects entrepreneurship. We first build a parsimonious model of bank screening and lending to ‘old’ and ‘young’ firms that are of heterogeneous quality and opacity. Banks can screen firms by either acquiring information about firms and their projects or by requiring collateral. Crucially, IT makes it relatively cheaper for banks to analyze hard information and in particular to use collateral in lending. Consequently, banks that have adopted IT more intensely are better equipped to lend to startups as these firms have not produced sufficient information (i.e. they are opaque ) and have to be screened through the use of collateral. The model thus predicts that IT in banking can spur entrepreneurship – and the more so when collateral value rises.

To test the model’s predictions, we use detailed data on the purchase of IT equipment of commercial banks across the United States in the years before the Great Financial Crisis (GFC). 2 Consistent with the model’s implications, we find that counties where IT-intensive banks operate experience stronger job creation by young firms, which, following previous literature, is used to measure local entrepreneurship. Moreover, the presence of IT-intensive banks strengthens the responsiveness of job creation by entrepreneurs to a rise of local real estate values. Using industry and state variation in the importance of collateral to obtain financing, we provide further evidence of a housing collateral channel for the impact of IT on entrepreneurship.

To measure IT adoption in banking, we follow seminal papers on IT adoption for non-financial firms, for example Bresnahan et al. (2002) , Brynjolfsson and Hitt (2003) , Beaudry et al. (2010) , or Bloom et al. (2012) . We use the ratio of PCs per employee within each bank as the main measure of bank-level IT adoption. This measure, while simple and based only on hardware availability, is a strong predictor of other measures of IT adoption, such as the IT budget or adoption of frontier technologies. 3 We also follow this previous literature in focusing on the overall adoption of information technology rather than specific technologies (e.g. ATMs or online banking as in Hannan and McDowell (1987) or Hernandez-Murillo et al. (2010) ) because of the multi-purpose nature of IT. Consistently, our analyses aim to shed light on the economic mechanisms behind the effects of IT adoption, rather than on the impact of specific IT applications.

We use these bank-level estimates to compute county-level exposure to banks’ IT based on banks’ historical geographic footprint. That is, a county’s exposure to banks’ IT is computed as the weighted average bank-level IT adoption of banks operating in a given county, with weights given by the historical share of local branches. Constructing local IT exposure based on banks’ historical footprint ameliorates concerns about banks’ selecting into counties based on unobservable county characteristics, such as economic dynamism or growth trajectories. Results show that county exposure is not systematically correlated with a large number of county-level characteristics, such as the unemployment rate or level of education, industry composition, or the use of IT in the non-financial sector.

We document that higher county-level IT exposure is associated with significantly higher entrepreneurial activity, measured as the employment share of new firms, following Adelino et al. (2017) . 4 Economically, our estimates imply that a one-standard-deviation higher IT exposure is associated with a 4 pp higher employment share in new firms (around 4% of the mean).

In principle, the positive relation between IT exposure and startup activity could be explained by reverse causality or omitted variable bias. Reverse causality is unlikely to be a major concern in our empirical setting: lending to startups represents only a small fraction of banks’ overall lending, which makes it unlikely that banks adopt IT solely because they expect an increase in startup activity. Yet, confounding factors could spuriously drive the association between IT and local entrepreneurship. For instance, a better-educated workforce may make it easier for banks to hire IT-savvy staff and also create more frequent business opportunities for new startups. To mitigate this concern, we start by including a wide set of county-level controls for differences in local characteristics, such as the industrial composition, education, income, and demographic structure. We document our results are unaffected by dropping the areas of the country where venture capital financing more present. We also control for IT adoption of non-financial firms, to avoid that entrepreneurship clustering in high-tech areas might drive the results. In fact, we do not find a positive correlation between IT adoption of non-financial firms and local entrepreneurship. Therefore, any confounding factors which bias our results by boosting both local entrepreneurship and IT in banking must act on banking only and not on IT adoption of other industries. This rules out many potential concerns, such as a positive correlation between entrepreneurship and local IT skills.

Additionally, when possible we include county fixed effects to control for observable and unobservable factors at the local level. Exploiting industry heterogeneity, we find that job creation by startups in counties more exposed to IT is relatively larger in industries that depend more on external financing ( Rajan and Zingales, 1998 ). This is true irrespective of whether we include county fixed effects or not – suggesting the relationship between entrepreneurship and IT is driven by better access to finance, and not unobservable county factors. Similarly, we estimate a long difference specification, in which we show that the local change in entrepreneurship over the course of our sample is positively associated with the increase in IT adoption of banks ex-ante present in the same county over the same time horizon, differencing out any potential observed and unobserved time-invariant county specific characteristics that could bias our results.

To further address the concern that exposure to IT could reflect other unobservable county characteristics, we develop an instrumental variable (IV) approach that exploits exogenous variation in banks’ market share across counties. Specifically, we instrument banks’ geographical footprint with a gravity model interacted with state-level banking deregulation, as in Doerr (2021) . That is, we first predict banks’ geographic distribution of deposits across counties with a gravity model based on the distance between banks’ headquarters and branch counties, as well as their relative market size ( Goetz et al., 2016 ). In a second step, predicted deposits are adjusted with an index of staggered interstate banking deregulation to take into account that states have restricted out-of-state banks from entering to different degrees ( Rice and Strahan, 2010 ). The cross-state and cross-time variation in branching prohibitions provides exogenous variation in the ability of banks to enter other states. Predicted deposits are thus plausibly orthogonal to unobservable county characteristics. The instrumental variable approach confirms that exposure to IT-savvy banks fosters local entrepreneurship. The estimated coefficients are not statistically different from the OLS estimates, indicating that the endogenous presence of high IT banks is not a significant concern for our empirical analysis.

Having established a robust relationship between local IT in banking and entrepreneur-ship, we investigate potential channels. Specifically, we focus on the importance of collateral, guided by our model that highlights the comparative advantage of high-IT banks to lend against collateral. While startups often do not have pre-existing internal collateral available to post against the loan, entrepreneurs often pledge their home equity as collateral. Following Mian and Sufi (2011) and Adelino et al. (2015) , we use changes in home value at the county-level to test whether higher collateral values foster startup activity, and how this relation depends on the presence of IT-intensive banks.

Consistent with the model’s predictions, we find a positive interaction between IT in banking and house price rises on entrepreneurship: the presence of IT-intensive banks spurs entrepreneurship more when collateral values rise. This interaction is strongest in industries where home equity is of high importance for startup activity measured by the industry propensity to use home equity to start and expand their business or the amount of startup capital required to start a business in an industry ( Hurst and Lusardi, 2004 ; Adelino et al., 2015 ; Doerr, 2021 ). Exploiting heterogeneity in the importance and price of collateral across regions and industries allows us to control for observed and unobserved time-variant and invariant heterogeneity at the county and industry level through granular fixed effects, further mitigating the concern that unobservable factors explain the correlation between IT in banking and entrepreneurship. Including granular fixed effects has no material effect on our estimated coefficients.

Recourse can partially substitute for the need of screening borrowers through collateral, as it may allow lenders to possess borrower assets or income, thereby diminishing the misalignment of interests between lenders and low-quality borrowers ( Ghent and Kudlyak, 2011 ). We therefore exploit that, in some states, banks are legally allowed (at least to some extent) to recourse borrowers’ income or other assets during a fore-closure, while in other states banks are prohibited from pursuing additional legal action in the event of a mortgage default (non-recourse states). Consistent with the model’s prediction, we show that the effect of IT in banking on entrepreneurship is stronger in non-recourse states due to the higher importance of collateral values. Also, the stronger elasticity of entrepreneurship to house prices in high IT counties, which we document for the whole sample, is muted in recourse states. This evidence further supports the importance of a housing collateral channel behind the stimulating role of IT in banking for entrepreneurship.

The model also predicts that IT in banking, while spurring local entrepreneurship, does not impact startup quality. The absence of a trade-off between financing more startups and lowering the quality of the marginal startup arises because more startups activity is caused by a better screening technology, while some form of screening is used for all borrowers. Empirically, we find no relation between IT exposure and job creation among young continuing firms (i.e. in the transition rates from 0 to 1 years old to 2 to 3 years old, or from 2 to 3 to 4 to 5 years old). This indicates that new firms in exposed counties are not more likely to exit in the next period. It also suggests that IT in banking can have a positive impact on business dynamism and productivity growth as the additional startups financed are of a similar quality.

In addition to county-level analyses, we use bank-county level data to shed further light on the role of the ability of IT adoption to improve the use of hard information. To this end, we focus on the importance of bank-borrower distance in lending. In fact, physical distance can increase informational frictions between borrowers and lenders, thereby increasing the importance of hard information that can be easily transmitted from local branches to the (distant) headquarters ( Petersen and Rajan, 2002 ; Liberti and Petersen, 2017 ; Vives and Ye, 2020 ). We study how distance affects bank lending in response to a local increase in business opportunities (i.e., a change in the demand for credit), measured by local growth in income per capita. We show that, first, banks’ small business lending is less sensitive to a local income shock in a county further away from banks’ headquarters – in line with the interpretation that a greater distance implies higher frictions. Consistent with the model, however, we find that banks’ IT adoption mitigates the effect of distance on the sensitivity of lending to a rise in business opportunities.

In a final step, we present additional evidence supporting the assumptions underlying the model. The model assumes that high IT banks have a relative cost advantage in lending against collateral, as they can better verify its value and transmit this information to the headquarters and also abstracts from the role of local competitions between banks. We therefore rely on loan-level data on corporate lending to show that banks with higher degree of IT adoption are more likely to request collateral for their lending, even controlling for borrower identity. This is consistent with a cost advantage of these banks with respect to other screening approaches. We finally analyze how our specifications are impacted by local market structure: we find no evidence that the relationship between IT and entrepreneurship is impacted by the local market concentration of the banking industry, indicating that the model’s simple approach to competition is appropriate for our research question (while, this interplay may be important for analyzing other issues, such as the impact on financial stability or intermediation costs ( Vives and Ye, 2020 ; De Nicolo et al., 2021 )).

The overall picture emerging from this paper is that a stronger reliance on information technology in banking decreases the consequences of informational frictions in lending markets, at least partly through making screening through the use of collateral more efficient. In turn, IT benefits opaque borrowers, such as startups, disproportionately more.

Literature and contribution. Our results relate to the literature investigating the effects of information technology in the financial sector on credit provision and small businesses. Banks’ increasing technological sophistication could enable them to more effectively screen and monitor new clients ( Hauswald and Marquez, 2003 ). On the other hand, more IT adoption could also increase banks’ reliance on hard information and growing lender-borrower distance ( Petersen and Rajan, 2002 ; Liberti and Mian, 2009 ; Liberti and Petersen, 2017 ). 5 Yet, while existing papers have often relied on proxies for banks’ use of technology or focused on specific technologies, little evidence exists on the direct impact of banks’ overall IT adoption on their lending, the role of collateral, or financing conditions of entrepreneurs.

Our work also relates to papers that analyze the importance of collateral for entrepreneurial activity ( Hurst and Lusardi, 2004 ; Adelino et al., 2015 ; Corradin and Popov, 2015 ; Schmalz et al., 2017 ). Problems of asymmetric information about the quality of new borrowers are especially acute for young firms that are costly to screen and monitor ( Degryse and Ongena, 2005 ; Agarwal and Hauswald, 2010 ). To overcome the friction, banks require hard information, often in the form of collateral, until they have better private information about borrowers ( Jimenez et al., 2006 ; Hollander and Verriest, 2016 ; Prilmeier, 2017 ; Vives and Ye, 2020 ). We contribute to the literature by providing first evidence that banks’ IT adoption increases the importance of collateral in banks’ financing of young firms. 6

Finally, we contribute to the recent literature that investigates how the rise of financial technology (FinTech) affects credit scoring and credit supply. Recent papers have focused on how FinTech has changed the way information is processed, as well as the consequences for credit allocation and performance; for instance, see Berg et al. (2019) ; Di Maggio and Yao (2018) ; Fuster et al. (2019) . However, the majority of papers examines the role of FinTech credit for consumers instead of businesses. While notable exceptions include Beaumont et al. (2199) ; Hau et al. (2018) ; Erel and Liebersohn (2020) ; Gopal and Schnabl (2020) , and Kwan et al. (2021) for the Covid-19 pandemic, the share of FinTech credit to small firms is still relatively small, compared to credit supplied by traditional providers (see Boot et al. (2021) for an overview). In this paper, we differentiate ourselves from the FinTech literature by focusing on traditional banks in the US, which are still a key provider of credit to small firms and have also invested heavily in IT. A further advantage of focusing on the banking sector is that our results are unlikely to be explained by regulatory arbitrage, which has been shown to be an important driver of the growth of FinTechs ( Buchak et al., 2018 ).

The remainder of the paper proceeds as follows. Section 2 presents a simple model of bank screening and lending. Section 3 provides an overview over our data. Section 4 presents empirical tests for the main implications of the model. Section 5 provides additional evidence supporting the model assumptions. Section 6 concludes.

We develop a simple model to assess the implications of bank IT adoption for screening and lending. A key building block is asymmetric information, whereby firm quality is initially unobserved by banks. To mitigate the arising adverse selection problem, banks can screen by acquiring information about firms to learn their type (unsecured lending) or by requesting collateral (secured lending). We describe the effect of the IT adoption of banks on lending to young firms and derive predictions tested in the subsequent analysis.

The agents in the economy are banks and firms. There are two dates t = 0,1, no discounting, and universal risk-neutrality. There are two goods: a good for consumption or investment and collateral that can back borrowing at date 0.

Firms have a new project at date 0 that requires one unit of investment. Firms are penniless in terms of the investment good but have pledgeable collateral C at date 0. Firms are heterogeneous at date 0 along two publicly observable dimensions. First, a firm’s collateral is drawn from a continuous distribution G(C ). The market price of collateral at date 1 (in terms of consumption goods) is P. Second, firms differ in their age: firms are either old (O) or young (Y), where we refer to young firms as entrepreneurs. In total, there is mass of firms normalized to one and the share of young firms is y E (0,1). For expositional simplicity, firm age and collateral are independent.

The key friction is asymmetric information about the firm’s type, that is the quality of the project. The project yields x > 1 at date 1 if successful and 0 if unsuccessful. Good projects are more likely to be successful: the probability of success is p G for good firms and p B for bad ones, where 0 < p B < p G < 1 and only good projects have a positive NPV, p B x < 1 < p G x. Project quality (the type G or B) is privately observed by the firm but not by banks. The share of good projects at date 0 is q > 0, which is independent of bank or firm characteristics. We assume that the share of good projects is low,

so the adverse selection problem is severe enough for banks to choose to screen all bor-rowers in equilibrium. As a result, all loans granted are made to good firms.

There is a unit mass of banks endowed with one unit of the investment good at date 0 to grant a loan. An exogenous fraction h ∈ (0,1) of banks adopted IT in the past and is therefore a high-IT bank, while the remainder is a low-IT bank.

Each bank has two tools to screen borrowers. First, the bank can pay a fixed cost F to learn the type of the project (screening by information acquisition). This cost can be interpreted as the time cost of a loan officer identifying the quality of the project. We assume that this cost is lower for old firms than for young firms: 7

which captures that old firms have (i) a longer track record and thus lower uncertainty about future prospects; or (ii) larger median loan volumes, so the (fixed) time cost is relatively less relevant.

Second, the bank can screen by asking for collateral at date 0 that is repossessed and sold at date 1 if the firm defaults on the loan. In this case, the bank does not directly learn the firm’s type, but the self-selection by firms – whereby only firms with good projects choose to seek funding from banks – also reveals their type in equilibrium. We assume that the cost of screening via collateral is lower for high-IT banks than for low-IT banks: 8

which captures that it is easier or cheaper for a high-IT bank to (i) verify the existence of collateral; (ii) determine its market value; or (iii) document or convey these pieces of information to its headquarters, consistent with hard information lending. Table A3 provides evidence consistent with this assumption, showing that high-IT banks issue more secured loans in the syndicated loans market.

We assume that banks and firms are randomly matched. The lending volume maximizes joint surplus, where banks receive a fraction θ ∈ (0,1) of the surplus generated. This assumption simplifies the market structure because it implies that a startup does not make loans application with multiple banks, thus muting competitive interaction between lenders. Our approach is supported by evidence that the degree of local concentration does not impact the relationship between IT and entrepreneurship (see Table A4 ).

In what follows, we assume a ranking of screening costs relative to the expected surplus of good projects:

In equilibrium, only good firms (a fraction q of all firms) may receive credit. Moreover, young firms (a fraction y of firms) receive credit only when matched with a high-IT bank (a fraction h of banks) and when possessing enough collateral, C > C min , which applies to a fraction 1 — G(C min ) of these firms. The bound on the collateral ensures the non-participation of bad firms (i.e. firms with a bad project), making it too costly for them to pretend to be a good firm. This binding incentive compatibility constraint defines C min :

where r is the bank’s lending rate. 9 Equation 5 has an intuitive interpretation: its left-hand side is the benefit of pretending to be a good type and receiving a loan from a bank, keeping the surplus x — r whenever the project succeeds, while the right-hand side is the cost of forgoing the market value of collateral when the project fails. Since the bad firm fails fairly often (low p B ), it is fairly costly for it to pretend to be a good firm. Note that the minimum level of collateral depends on its price, C min = C min (P ). In sum, sufficient collateral, C > C min , ensures that only good firms receive loans in equilibrium.

Old firms always receive credit. When matched to a high-IT bank, lending is backed by collateral if the old firm has enough of it, otherwise the high-IT bank ensures the old firm is of good quality via information acquisition. When matched with a low-IT bank, screening via information acquisition is used. (For a relaxation, see Extension 2 below.)

Taken these results together, we can state the model’s implications about the share of expected lending to young firms s Y and how it depends on the share of high-IT firms h, the price of collateral P, and both factors simultaneously.

Proposition 1 The share of lending to young firms is s Y = y h [ 1 − G ( C min ) ] 1 − y + y h [ 1 − G ( C min ) ]

We state comparative static results in terms of the first three predictions.

Prediction 1 . A higher share of high-IT banks increases the share of lending to young firms, d s Y d h > 0 .

Prediction 2 . A higher collateral value increases the share of lending to young firms, d s Y d P > 0 (which is consistent with the evidence documented in Adelino et al. (2015) ).

Prediction 3 . A higher collateral value increases the share of lending to young firms more when the share of high-IT banks is higher, d 2 s Y d h d P > 0 .

To gain intuition for these predictions, note that a higher share of high-IT banks implies that good young firms with sufficient collateral can receive funding more often. A higher value of collateral, in turn, increases the range of young firms that have sufficient collateral, increasing expected lending on the extensive margin (lower C min ).

In equilibrium, all potential borrowers are screened and only good projects are financed, regardless of the screening choice or the bank type. Thus, the model implies that IT adoption does not affect the quality of firms who are funded by banks, as summarized in the following prediction.

Prediction 4 . Bank IT adoption does not affect the quality (default rate) of firms receiving funding in equilibrium.

We will test the model’s predictions below. Some implications are also consistent with evidence documented in other work. For instance, young firms use more collateral than old firms in equilibrium. Since firm age and size are correlated in the data, this implication is consistent with recent evidence on the greater importance of collateral for lending to small businesses ( Chodorow-Reich et al., 2021 ; Gopal, 2019 ).

Extension 1: Recourse versus non-recourse states . To tease out additional model implications, we consider the difference between recourse and non-recourse states. To do so, we assume that a fraction i ∈ (0,1) of firm owners generate an additional external income I ≥ PC min and banks may have have recourse to this income. Banks of all types (in recourse states) can obtain this income, while only high-IT banks have the comparative advantage in lending via collateral (as in the main model). In non-recourse states, no bank can lay claim to I upon the failure of the project (and loan default).

To understand the implications of recourse, note that collateral and recourse to future income are substitutes in deterring bad firms from pretending to be good ones. That is, firms with low collateral but (high) future income can obtain a loan from either bank type, while firms with high collateral and no future income can obtain a loan only from high-IT banks (as in the main model). The next prediction follows immediately.

Prediction 5 . A higher share of high-IT banks increases the share of lending to young firms by less in recourse states than in non-recourse states, d s Y d h N o n − r e c o u r s e > d s Y d h Re c o u r s e . Similarly, the impact of higher collateral value when the share of high-IT banks is higher is lower in recourse states than in non-recourse states, d 2 s Y d h d P N o n − r e c o u r s e > d 2 s Y d h d P Re c o u r s e .

Extension 2: Distance . A large literature in banking deals with the importance of distance between lenders and borrowers and the role of soft information. In our model, high-IT banks have a comparative advantage in screening based on collateral, which can be interpreted as hard information lending (and is thus unaffected by distance). Low-IT banks lend based on information acquisition. To allow for a role of distance, we assume in this extension that low-IT banks can screen some young firms, namely those that are close. Hence, we relax Assumption 4 by assuming

where the cost of information acquisition is low enough relative to the expected surplus of a good project when the firm is close to the bank.

Prediction 6 . Distance matters less for high-IT banks than low-IT banks.

In particular, the advantage of high-IT banks in hard information lending makes its lending insensitive to distance, for example in response to a shock to local economic conditions. By contrast, distance matters for the lending of low-IT banks, as they can only accommodate an additional demand for funds from young firms close to the bank.

• 3 Sample and Variable Construction

This section explains the construction of our main variables and reports summary statistics. Our main analysis focuses on the years from 1999 to 2007. While banks continued to adopt IT in more recent years, the post-crisis period saw substantial financial regulatory reform (such as the Dodd-Frank Act and regular stress tests), both of which have affected banks’ ability to lend to young and small firms. The absence of major financial regulatory changes during our sample period makes it well-suited to identify the effects of banks’ IT on entrepreneurship.

IT adoption . Data on banks’ IT adoption come from an establishment-level survey on personal computers per employee by CiTBDs Aberdeen (previously known as “Harte Hanks”) for the years 1999, 2003, 2004, and 2006. We focus on establishments in the banking sector (based on the SIC2 classification and excluding savings institutions and credit unions). We end up with 143,607 establishment-year observations.

Our main measure of bank-level IT adoption is based on the use of personal computers across establishments in the United States. To construct county-level exposure to bank IT adoption, we proceed as follows. We first hand-merge the CiTBD Aberdeen data with data on bank holding companies (BHCs) collected by the Federal Reserve Bank of Chicago. We use the Financial Institution Reports, which provide consolidated balance sheet information and income statements for domestic BHCs. We then compute a BHC-level measure of IT adoption based on a regression of the share of personal computers on a bank (group) fixed effect controlling for the geography of the establishment and other characteristics. 10 We define this measure as I T ˜ b . The focus on BHCs rather than local branches or banks is due to the facts that (a) most of the variation in branch-level IT adoption is explained by variation at the BHC-level, (b) technology adoption at individual branches could in principle be influenced by the rate of local firm formation, (c) using a larger pool of observations reduces measurement error, and (d) this estimation procedure yields bank-level IT adoption measures that are uncorrelated with a bank’s business model (assets or funding), size, or profitability, suggesting this approach is able to purge any potential correlation between IT and management quality or other confounding factors ( Pierri and Timmer, 2020 ).

We then merge the resulting Aberdeen-BHC data set to the FDIC summary of deposits (SOD) data set that provides information on the number of branches (and deposits) of each bank in a county. To construct a measure on local exposure to IT adoption of banks, we combine I T ˜ b with the branch distribution of each bank in 1999, thus before the period of analysis. We then define the average IT adoption of all banks present in a county by:

where No.Branches b,c is the number of branches of bank b in county c in 1994 and No.Branches b,c is the total number of branches across all banks in 1994 for which we have I T ˜ b available. To ease interpretation, IT c is standardized with mean zero and standard deviation of one. Higher values indicate that banks with branches in a given county have adopted relatively more IT. 14

Our main measure of IT adoption is based on the use of personal computers across establishments in the United States, as this variable has the most comprehensive coverage during our sample period. However, for the year 2016, we also have information on the IT budget. The correlation between the IT budget of the establishment and the number of computers as a share of employees is 0.65 in 2016. The R-squared of a cross-sectional regression of PCs per Employee on the per capital IT budget is 44%. There is also a positive correlation between PCs per Employee and the probability of adoption of cloud computing. These correlations provide assurance that the number of personal computers per employee is a valid measure of IT adoption. The ratio of PCs per employee has also been used by seminal papers, for example Bresnahan et al. (2002) , Brynjolfsson and Hitt (2003) , Beaudry et al. (2010) , or Bloom et al. (2012) .

County and industry data . Data on young firms are obtained from the Quarterly Workforce Indicators (QWI). QWI provide detailed data on end-of-quarter employment at the county-two-digit NAICS industry-year level. Importantly, they provide a break-down by firm age brackets. For example, they report employment among firms of age 0–1 in manufacturing in Orange County, CA. Detailed data are available from 1999 on-ward. QWI are the only publicly available data set that provides information on county employment by firm age and industry.

We follow the literature and define young firms or entrepreneurs as firms aged 0–1 ( Adelino et al., 2017 ; Curtis and Decker, 2018 ; Doerr, 2021 ). For each two digit industry in each county, we use 4th quarter values. Note that the employment of young firms is a flow and not a stock of employment, as it measures the number of job created by new firms in a given year. In our baseline specification, we scale the job creation of young firms by total employment in the same county-industry cell, but results are unaffected by other normalization choices. Figure 1 (panel b) plots the average creation of jobs by startup in the United States between 2000 and 2006, showing great variability both between and within states and underscoring that tech hubs, such as the Silicon Valley, are not the areas where such job creation is more prevalent. It also highlights that, while this data covers most of US surface, information is unavailable for counties in Massachusetts during the period of study.

Spatial distribution of startups and IT exposure

Citation: IMF Working Papers 2021, 214; 10.5089/9781513591803.001.A001

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The 2007 Public Use Survey of Business Owners (SBO) provides firm-level information on sources of business start-up and expansion capital, broken down by two-digit NAICS industries. For each industry i we compute the fraction of young firms that reports using home equity financing or personal assets (home equity henceforth) to start or expand their business, out of all firms ( Doerr, 2021 ). In some specifications we split industries along the median into high- and low-home equity dependent industries.

County controls include the log of the total population, the share of black population and share of population older than 65 years, the unemployment rate, house price growth, and log per capita income. The respective data sources are: Census Bureau Population Estimates, Bureau of Labor Statistics Local Area Unemployment Statistics, Federal Housing Finance Agency (FHFA) House Price Index (HPI), and Bureau of Economic Analysis Local Area Personal Income. 12

Bank data . The Federal Deposit Insurance Corporation (FDIC) provides detailed bank balance sheet data in its Statistics on Depository Institutions (SDI). We collect second quarter data for each year on banks’ total assets, Tier 1 capital ratio, non-interest and total income, total investment securities, overhead costs (efficiency ratio), non-performing loans, return on assets, and total deposits.

To capture the response of small business lending to changes in local house prices, we exploit Community Reinvestment Act (CRA) data on loan origination at the bank-county level, collected by the Federal Financial Institutions Examination Council at the subsidiary-bank level. The CRA data contain information on loans with commitment amounts below $1 million originated by financial institutions with more than $1 billion in assets. We aggregate the data to the BHC-county level. To mitigate the effect of outliers we normalize the year-to-year change in lending volume by the mid-point of originations between the two years:

where b refers to BHC, c to county and t to year. This definition bounds growth rates to lie in [—2, 2], where —2 implies that a bank exited a county between t — 1 and t, and 2 that it entered. 13

Descriptive statistics . Table 1 reports summary statistics of our main variables at the county level; Table 2 further reports the balancedness in terms of county-level covariates, where we split the sample into counties in the bottom and top tercile of IT exposure. Except for population, we do not find significant differences across counties. Counties with high and low exposure to IT banks are similar in terms of their industry employment structure, but also in terms of the IT adoption of non-financial firms in the county. The absence of a correlation between IT exposure to banks and other county-specific variables is reassuring as it suggests that the exposure to IT in banking is also uncorrelated with other unobservable county characteristics that could bias our results. 14

Descriptive statistics

Balancedness at the county level

4 Testing the Model’s Predictions

This sections proposes a set of empirical tests for the main predictions of the model described in Section 2.

• 4.1 IT exposure and local entrepreneurship (Prediction 1)

The first prediction of the model implies a positive relation between the share of high-IT banks in a market and local entrepreneurship.

Prediction 1 . d s Y d h > 0 : a larger local presence of high-IT banks increases local lending to young firms .

To investigate this prediction, we estimate the following cross-sectional regression at the county-industry level:

The dependent variable is the employment share of firms of age 0–1 (startups) out of total employment in county (c) and 2-digit industry (i), averaged over 1999–2007. IT exposure c denotes county exposure to IT-intensive banks as of 1999, measured by the IT adoption of banks’ historical presence in the county. It is standardized to mean zero and a standard deviation of one.

To mitigate the concern that the relationship between exposure to IT in banking and local entrepreneurship is a spurious correlation driven by other local characteristics, we include a rich set of county-level controls. Controlling for county size (log of the total population) we avoid comparing small isolated counties to large urban ones. We further control for the share of population age 65 and older as younger individuals may be more likely to start companies and also have better IT knowledge. Other socio-demographic controls, such as the share of the black population, the unemployment rate, and the median household income, purge our estimates from a potential correlation between local income or investment opportunities and the variables of interests. We also control for the industrial structure of the county (proxied by employment shares in the major 2-digit industries 23, 31, 44, 62, and 72) in order to compare counties that are similar from the economic point of view, and are subject to similar shocks ( Bartik, 1991 ). We also control for the share of adults with bachelor degree or higher, as human capital may spur entrepreneurship ( Bernstein et al., 2021 ) and could also make it easier to adopt IT. Finally, we control for IT in non-financial firms (measured as the average PCs per employee in non-financial firms) to tackle the concern that startup activity may thrive in location where IT is more readily available, perhaps because many promising startups operate in the IT space or use new technology to quickly scale up. 15 All variables are measured as of 1999. Standard errors are clustered at the county level, and regressions are weighted by county size.

Abstracting from interaction terms, Prediction 1 implies that α 1 > 0. Before moving to the regression analysis, Figure 2 shows the relation between IT exposure and startup employment in a nonparametric way. It plots the share of employment among firms age 0–1 on the vertical axis against county exposure on the horizontal axis and shows a significant positive relationship, consistent with Prediction 1. We now investigate this pattern in greater detail.

Job Creation by Young Firms and Banks’ IT adoption

Table 3 shows a positive relation between county IT adoption and startup activity. Column (1) shows that counties with higher levels of IT exposure also have a significantly higher share of employment among young firms. Column (2) shows that the coefficient remains stable when we add county-level controls. Column (3) includes industry fixed effects (at the NAICS2 level) to control for unobservable confounding factors at the industry level. Including these fixed effects does not change the coefficient of interest in a statistically or economically meaningful way, despite a sizeable increase in the R-squared by 40 pp. This pattern suggests that local IT exposure is orthogonal to industry-specific characteristics ( Oster, 2019 ). The magnitude of the impact is sizeable: In column (3), a one standard deviation higher IT exposure is associated with a 0.38 pp increase in the share of young firm employment (4% of the mean of 9.3%).

County IT exposure and entrepreneurship

IT in Banking and Startup Rate – Differences

Job Creation by Young Firms, Banks’ IT adoption, House Prices, and Home Equity

In the model, banks’ IT spurs entrepreneurship through a lending channel, so we expect the positive correlation shown in columns (1)-(3) to be stronger in industries that depend more on external finance. We therefore augment the regression with an interaction term between IT adoption and industry-level dependence on external finance (which, as in Rajan and Zingales (1998) , is measured by capital expenditure minus cash flow over capital expenditure). This is, in Equation (10), we expect β 3 > 0. In column (4), the coefficient on the interaction term between IT adoption and external financial dependence is positive, and economically and statistically significant. Counties with higher IT exposure have a higher share of employment among young firms precisely in those industries that depend more on external finance, consistent with the notion that the correlation is driven by the impact of banks’ IT on startups’ financing. In terms of magnitude, a one standard deviation higher IT exposure is associated with a 1 pp increase in the share of young firm employment in industries that depend on external finance (11% of the mean).

So far, the regressions included industry fixed effects to purge the estimation from observable and unobservable confounding factors at the industry level. In column (5), we further enrich our specification with county fixed effects to control for confounding factors at the local level, for example changes in consumption of government expenditure. Results are remarkably similar to column (4): the inclusion of county fixed effects changes the estimated impact of IT exposure interacted with financial dependence by only 0.02 pp – despite the fact that the R-squared increases by 12 pp. Results from columns (2)-(3) and (4)-(5) suggest that IT exposure is uncorrelated with observable and unobservable county and industry characteristics, reducing potential concerns about self-selection and omitted variable bias ( Altonji et al., 2005 ; Oster, 2019 ).

Taken together, Figure 2 and Table 3 provide support for Prediction 1: a larger local presence of IT-intensive banks is associated with more startup activity, and especially so in sectors that depend more on external financing.

Robustness . A set of robustness tests is presented in Table A1 . Column (1) is the baseline (as column (3) of Table 3 ). In column (2) the IT exposure measure is the unweighted average of the IT adoption of banks that operate in a county, rather weighted by banks’ number of branches in that county. Column (3) uses an alternative exposure measure that use the share of local deposits from FDIC, rather than the number of branches, as a weighting variable. The results of these empirical exercises are in line with baseline and thus highlight that our findings are not driven by any specific choice of the construction of the IT adoption measure. Column (4) excludes employment in startups in the financial and education industries, showing financial companies or universities are not driving our results. Column (5) excludes Wyoming which, perhaps surprisingly, the state with the highest exposure to banks’ IT adoption (see Figure 1 , panel b). Column (6) includes state fixed effects, showing that our results are driven by within-state variation, rather than variation between different part of the county. Column (7) shows robustness of the specification by normalizing the share of employment in startups by previous year’s total employment. Column (8) reveals that our results are due to an impact on the numerator (employment of startups) rather than denominator (total employment).

Our model underscores the role of IT as a technology to facilitate the use of entrepreneurs’ real estate as collateral. However, local economic conditions could also be correlated with collateral values and this may create a correlation between local demand and use of collateral. This concern should be mitigated by the fact that we directly control for local income. Additionally, we test whether our main findings is present in industries which are less impacted by local economic conditions, that is “tradable” industries. We rely on the tradable classification of 4 digit industries by Mian and Sufi (2014) , which we aggregate at out 2 digit level: two of the 2 digit industries, that is manufacturing and mining and extraction, have most of their employment in tradable sub-industries. As illustrated by column (9) the relationship between IT and entrepreneurship is much stronger within these industries than in baseline, suggesting it is not driven by local demand. As these industries have also high dependence on external finance, this finding further suggest our main result is driven by access to finance rather than local demand.

We then consider the concern that other forms of external financing, venture capital (VC) in particular, may be correlated with IT in banking and have an impact on our results. We exploit the fact that VC funding is highly concentrated in a small fraction of the US territory. 16 We thus repeat our regressions excluding the top 20 counties (representing almost 80% of VC funding at the time) or 7 states with more VC presence, and find results similar to baseline, see columns (10) and (11).

We finally investigate the potential role of data coverage in the analysis. In fact, the IT variable is constructed from survey rather than administrative data. The high quality of the survey collected by Harte-Hanks/Aberdeen over a few decades is disciplined by market forces as the information are sold to IT supplier to direct their marketing efforts. However, it is still possible that the survey effort or success might be heterogeneous across different locations. We therefore compute a measure of local coverage, which is equal to the ratio between the establishments belonging to the banking industry surveyed by the marketing company in a county in a year and the total number of branches present according to FDIC data. We then average these across the four years (1999, 2003, 2004, 2006) to have a measure of average coverage for each county. The average value is 13.6%, with a standard deviation of 8.4%. To test how heterogeneity in local coverage might impact our results we drop the counties in the bottom quartile of coverage or, also include coverage as a control. Results are robust as reported by the last two columns.

Instrumental variable approach . The inclusion of detailed controls and the across-industries heterogeneity approach ( Rajan and Zingales, 1998 ) help mitigate the concern that local factors might impact both the presence of high IT banks and entrepreneurship. Yet, IT exposure could still be correlated with such local unobservable factors, preventing us from drawing causal implications. To this end, we follow Doerr (2021) and adopt an instrumental variable approach. In a first step, we predict banks’ geographic distribution of deposits across counties with a gravity model based on the distance between banks’ headquarters and branch counties, as well as their relative market size ( Goetz et al., 2016 ). In a second step, predicted deposits are adjusted with an index of staggered interstate banking deregulation to take into account that states have restricted out-of-state banks from entering to different degrees ( Rice and Strahan, 2010 ). The cross-state and cross-time variation in branching prohibitions provides exogenous variation in the ability of banks to enter other states. Predicted deposits are thus plausibly orthogonal to unobservable county characteristics during our sample period. We thus compute a >predicted county-level measure of exposure to IT in banking as:

We estimate a two-stage least square model considering ITc as an endogenous regressor and I T ^ c as an excluded instrument. Using I T ^ c as an instrument allows us to purge our specification from the bias introduced by unobservable factors that might attract high-IT banks and also impact local startup activity. Results are presented in Table 4 . Column (1) presents the baseline estimate on this sample of counties. Column (2) is the first stage and shows a positive correlation between exposure to IT and predicted exposure to IT. Column (3) is the reduce-form regression of the instrument on the variable of interest, showing a positive impact of predicted exposure to IT in banking on entrepreneurship. Finally, column (4) is the second stage regression: the IV estimate of the impact of IT in banking on entrepreneurship is qualitatively similar than baseline and larger in magnitude. However, we cannot reject the null hypothesis that the difference between OLS and IV estimates is zero, suggesting biases coming from unobservable factors at the local level are not significantly biasing the baseline estimates.

County IT exposure and entrepreneurship: IV approach

Increase in IT adoption over time . The period of study also is a time of robust technology adoption in the banking sector. Thus, another approach to test Prediction 1 is to analyze the relationship between increase in IT adoption and change in entrepreneurship at the county-level. To do so we compute the county exposure as

where Δ I T ˜ c is the increase of IT adoption between 1999 and 2006 of bank b.

We find that counties more exposed to the increase in IT in banking also experienced less negative decreases in startup rates, as illustrated by Figure 3 . The positive correlation between changes in IT adoption in banking and changes in startup rates is also confirmed by more formal regression analysis presented in Table A2 . These results further confirm Prediction 1 . Moreover, this first-difference approach implicitly controls any county-level (time invariant) observable and unobservable characteristics by differencing them out.

• 4.2 IT, house prices, and entrepreneurship (Predictions 2 & 3)

A large literature highlights the importance of the collateral channel for employment among small and young firms: rising real estate prices increase collateral values, thereby mitigating informational frictions and relaxing borrowing constraints ( Rampini and Viswanathan, 2010 ; Adelino et al., 2015 ; Schmalz et al., 2017 ; Bahaj et al., 2020 ). The role of collateral in our model is directly related to this literature. Predictions 2 & 3 of the model predict the following relationships between entrepreneurship, collateral values, and IT adoption:

Prediction 2 . d s Y d P > 0 : a higher collateral value increases the share of lending to young firms .

Prediction 3 . d 2 s Y d h d P > 0 : a higher collateral value increases the share of lending to young firms more when the share of high-IT banks is higher .

The predictions are tested in this section by examining how local IT adoption affects the sensitivity of entrepreneurship to house prices using a panel of county-year observations. To complement this analysis, Appendix A1 presents evidence at the bank-county-year level that high IT banks’ small business lending reacts more to an increase in local house prices.

We estimate the following regression at the county-industry-year level from 1999 to 2007:

The dependent variable is the employment share of firms of age 0–1 out of total employment in county (c) and 2-digit industry (i) in given year (t). IT exposure c denotes counties’ IT exposure as of 1999, standardized to mean zero and a standard deviation of one. ΔHPI c t is the yearly county-level growth in house prices. Controls (included when county fixed effects are not) are county size (log total population), the share of population age 65 and older, the share of black population, education, the unemployment rate, the industrial structure (measured by employment shares in the major 2-digit industries 23, 31, 44, 62, and 72), and IT adoption in non-financial firms (PCs per employee in non-financial firms), all of which lagged by one period. Standard errors are clustered at the county level.

Prediction 1 implies that γ 2 > 0. Table 5 reports the estimation results. To start, column (1) shows that higher IT exposure is associated with a higher share of young firm employment in the cross-section – in line with Table 3 . We then explicitly test Prediction 2 . Column (2) shows that a rise in house prices is associated with an increase in entrepreneurship at the local level, conditional on year fixed effects that absorb common trends. Column (3) confirms this finding when controlling for IT adoption at the county level. These findings provide support for Prediction 2 .

County IT exposure, entrepreneurship, and collateral

We then test Prediction 3 by augmenting the equation with an interaction term between changes in local house prices and county exposure to IT in banking. That is, we focus on the coefficient γ 3 in Equation 13. Based on Prediction 3, we expect γ 3 > 0. To isolate the variation of interest and controlling for any confounding factor at the local or industry level, we include county-industry fixed effects and exploit only the variation within each county-industry cell – the coefficient on IT exposure is now absorbed. As reported in column (4) of Table 5 , we find γ 3 > 0, consistent with Prediction 3 . Columns (5) and (6) add time-varying county controls, as well as industryxyear fixed effects that account for unobservable changes at the industry level. The interaction coefficient remains positive and similar in size across specifications.

Previous literature has highlighted that young firms are more responsive to changes in collateral values in industries in which average start-up capital is lower, or in industries in which a larger share of firms relies on home equity to start or expand their business ( Adelino et al., 2015 ; Doerr, 2021 ). Therefore, we exploit industry heterogeneity to provide further evidence on Prediction 3 . Focusing on differences between industries within the same county and year allows us to control for industryx year and countyx year fixed effects and thus purge our estimates from the impact of any time-varying industry or local shock. Results, presented in columns (6) and (7), reveal that the larger benefits of house prices increase due to the presence of high IT banks occur exactly in those industries whose financing should be more sensitive to changes in collateral values.

In sum, Table 5 provides evidence in line with Predictions 1 and 2: entrepreneurship increases when local collateral values increase, and do so in particular in counties with higher exposure to IT-intensive banks.

• 4.3 IT exposure and startup quality (Prediction 4)

The model predicts that a stronger presence of high IT banks increases the share of startups receiving funding without impacting its average quality. As IT helps with screening, there is no trade off between quantity of credit and marginal quality of the borrower.

Prediction 4 . Bank IT adoption does not affect the quality (default rate) of firms receiving funding in equilibrium .

In the model firm quality is disciplined by the probability of default. In the data, we use the average growth rate of employment of startups during the first few years of life, which can be proxied by the “transition rates” ( Adelino et al., 2017 ). As the QWI report the employment of firms that are 2–3 years old in a given year, we can substract the employment of startups (firms age 0 or 1 year) two years before to obtain the change in the job created by the continuing startups in that period, which equals the (weighted) average growth rate of employment among those firms. Thus, the transition rate in a county-industry cell is defined as:

We construct similar transition rates for firms transitioning from 2–3 years to 4–5 years. We then estimate a cross-sectional regression similar to Equation 10 where the dependent variable is the average of transition c,s,t between 2000 and 2006. As illustrated in columns (1)-(3) of Table 6 , there is no correlation between a county’s exposure to IT in banking and the growth rate of local startups, neither on average nor in industries that are more dependent on external finance. We find similar effects for the transition rates from 2–3 years to 4–5 years in columns (4)-(6).

County IT exposure and transition rates

The lack of a significant relationship between IT exposure and local startup quality matters, as it suggests our findings have aggregate implications. In fact, if the additional startups created thanks to IT are not of lower quality than other startups, they should be able to bring benefits to the economy, for example in terms of business dynamism and long-run employment creation and productivity growth.

• 4.4 IT and the role of recourse default (Prediction 5)

Collateral and other screening mechanisms can be partially substitute by deficiency judgment if the lender is able to possess other borrower’s assets or future income through a deficiency judgment. This makes it less appealing for potential entrepreneurs with a low quality project to pretend to be of good type and get funds, diminishing the extent of asymmetric information between lenders and borrowers. As IT spurs entrepreneurship by allowing for better screening, the model predicts that IT is less important when recourse default is possible ( Prediction 5 ). Similarly, the stronger elasticity of entrepreneurship to house prices in counties more exposed to IT in banking ( Prediction 3 ) is predicted to be more muted.

There is a significant heterogeneity across US states in terms of legal and practical considerations which makes obtaining a deficiency judgment more or less difficult for the lender. Ghent and Kudlyak (2011) relies on recourse / non-recourse classifications of states from the 21 st edition (2004) of the National Mortgage Servicer’s Reference Directory to show that recourse clauses impact borrowers’ behavior. We rely on the same classification and estimate the cross-sectional relationship between IT and entrepreneur-ship (i.e. Equation 10) for counties in recourse versus non-recourse states. Comparison between columns (1) and (2) of Table 7 highlights that the relationship is stronger within non-recourse states, as predicted by the model. Moreover, we can test for whether this difference is statistically significant by adding state fixed effects and the interaction term between exposure to IT and state recourse classification to Equation 10. Columns (3) shows that in recourse states the relationship between IT adoption and entrepreneurship is significantly weaker. Column (4) confirms the finding excluding North Carolina, as its classification presents some ambiguity. Moreover, while in the whole sample entrepreneurship responds more to house prices in counties more exposed to IT in banking, this is less the case in recourse states, see the last column of Table 5 .

• 4.5 IT and the role of distance (Prediction 6)

In the model, banks verify the value of collateral at cost v. We assume that v is lower for high-IT banks because they can better verify the existence and market value of collateral, but also because they it is cheaper for high-IT to transmit such information about borrowers’ collateral to (distant) headquarters of high-IT banks. Following a large literature that shows that informational frictions increase with lender-borrower distance ( Liberti and Petersen, 2017 ), we now investigate the importance of distance in banks’ lending decisions. The literature suggests that IT adoption by banks could reduce the importance of distance ( Petersen and Rajan, 2002 ; Vives and Ye, 2020 ), as it enables a more effective transmission of hard information. Consequently, the informational frictions associated with distance become less important. To test whether the relationship between local investment opportunities and lender-borrower distance is different for banks’ with more or less IT use, we consider the following model that relates banks’ loan growth to local investment opportunities (measured as the change in local income, proxying an increase in local demand for credit):

The dependent variable is the log difference in total CRA small business loans by bank b to borrower county c in year t along the intensive margin. 17 log(distance) measures the distance between banks’ HQ and the county of the borrower. In general, we expect that an increase in local investment opportunities, measured by the log difference of county-level income per capita, increases local lending; and the more so, the smaller the log distance between the lender and the borrower. That is, we expect β 1 > 0 and β 3 ̼ 0. As banks’ IT adoption reduces the importance of distance, the model predicts β 3 to be significantly smaller for high IT banks.

Results in Table 8 support these hypotheses. Column (1) shows that rising local incomes are indeed associated with higher local loan growth; and that distance reduces the sensitivity of banks’ CRA lending in response to local investment opportunities as the interaction terms between changes in income and distance is negative. This findings holds when we include county x year fixed effects to control for any local shock in column (2). Columns (3) and (4) show that the lower responsiveness of banks’ lending to income shocks in counties located further away is present only for low IT banks; for high IT banks, distance has no dampening effect. Interaction specifications in columns (5) and (6) confirm this finding: while distance reduces the sensitivity of lending to changes in local investment opportunities for low IT banks, among high IT banks distance matters significantly less in the decision to grant a loan in response to local shocks to investment opportunities. Results are similar when we enrich the specification with bank fixed effects.

CRA lending — distance all loans

• 5 Competition and Collateralized Lending

In this section we present additional evidence that speaks to assumptions and implications of the model. We provide evidence that high-IT banks are more likely to provide col-lateralized loans even when controlling for unobservable borrower characteristics through fixed effects, supporting the assumption that IT provides an advantage in collateralized lending. We also show that the effects of IT on startup activity and lending do not depend on local competition among banks.

IT and the use of collateral . Our model builds on the assumption that high IT banks have a relative cost advantage in screening through collateral with respect to information acquisition. We investigate the soundness of this assumption by looking at whether banks which adopt more IT are also more likely to use collateral in their lending, controlling for borrower characteristics. While we do not have loan-level information on lending to startups, as a second best we can perform such empirical test on large corporate loans data from DealScan as in Ivashina and Scharfstein (2010) , for example.

Consistent with the model’s assumption, Figure A2 shows that the share of loans that are collateralized is positively correlated with bank IT adoption. To test whether this correlation is really driven by banks’ IT rather than borrowers heterogeneity, we estimate the following linear probability model:

where b is a bank that granted a loan in year t to (large) corporate borrower i and secured b,i,t is a dummy equal to one whenever the loan is collateralized. Results are presented in Table A3 and confirm that more IT intense banks are more likely to lend through a secured loan than other banks, even when controlling for borrower fixed effects.

The role of local competition . The model assumes that local bank competition is independent of bank IT adoption. In fact, bank and potential borrowers are assumed to be matched and to share the surplus from lending – if a loan is granted. To understand how this simplified market structure might impact our results, we reestimate the main equation of interest, Equation 10, and augment it with a term for bank concentration (in terms of deposits or CRA lending) in a county, and the interaction between local IT exposure and concentration. Results are presented in Table A4 . Higher concentration is associated with more startup activities. This might be due to the fact that banks might be more prone to lend to startups when competition is low if they know they can gain larger information rent and extract more surplus as these firms grow ( Petersen and Rajan, 1995 ). However, we find no significant interaction between concentration and local IT adoption in banking. The positive impact of IT on startups does not seem to depend on the local market structure. This result mitigates the concern that the simplistic approach to market power in the model is severely harming its ability to describe the relationship between IT adoption and entrepreneurship, which is the aim of this paper.

• 6 Conclusion

Over the last decades, banks have invested in information technology at a grand scale. However, there is very little evidence on the effects of this ‘IT revolution’ in banking on lending and the real economy. In this paper we focus on startups because of their importance for business dynamics and productivity growth, and because they are opaque borrowers and thus may be sensitive to technologies that change information frictions.

We show that IT adoption in the financial sector has spurred entrepreneurship. In regions where banks that with more IT-adoption have a larger footprint, job creation by startups was relatively stronger; this relationship is particularly pronounced in industries that rely more on external finance. We show – both theoretically and empirically – that collateral plays an important role in explaining these patterns. As IT makes it easier for banks to assess the value and quality of collateral, banks with higher IT adoption are more likely to lend against increases in entrepreneurs’ collateral.

Our results have important implications for policy. Banks’ enthusiasm towards technology adoption has been very strong during the last years, 18 and the role of FinTech companies as lenders of small businesses has been increasing since the GFC ( Gopal and Schnabl, 2020 ). This has triggered a debate on the impact of IT in finance on the economy, for example through its impact on the need for collateral and firms’ access to credit ( Gambacorta et al., 2020 ). Our findings suggest that IT in lending decisions can spur job creation by young firms by making lending against collateral cheaper. From a policy perspective, this finding raises the hope that improvements in financial technology help young and dynamic firms to get financing.

Given the strong rise in house prices since the pandemic and larger reliance on IT systems due to a reduction in physical interactions, our evidence also suggests that the adoption of IT in banking can spur entrepreneurship and productivity growth in the post-pandemic world.

Adelino , Manuel , Song Ma , and David Robinson ( 2017 ) “ Firm age, investment opportunities, and job creation ”, The Journal of Finance , 72 ( 3 ), pp. 999 – 1038 .

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• A1 Banks’ IT Adoption and Small Business Lending

To provide further evidence on how banks’ IT affects access to finance for entrepreneurs, we investigate how high- and low-IT banks adjust their small business lending in response to house price changes. We estimate the following regression equation from 1999 to 2007 at the bank-county-year level:

The dependent variable is the growth in total CRA small business loans by bank b to borrower county c in year t. We follow Davis and Haltiwanger (1999) and compute the growth rate along the extensive margin that accounts for bank entry into and exit out of counties over the sample period. The main explanatory variable IT b measures the use of IT at the bank level, as described in Section 3. ∆HPI c t measures the yearly change in house prices. County-level controls are the same as in Equation 13, while bank-level controls are the log of assets, deposits over total liabilities, the share non-interest income, securities over total assets, return on assets, the equity ratio (Tier 1), and the wholesale funding ratio. We cluster standard errors at the county level to account for serial correlation among banks lending to the same county.

If banks that use IT more rely more on hard information, as indicated by the count-level analysis, we expect their lending to be more sensitive to changes in local collateral values, i.e. changes in local house prices rise. That is, we expect β 3 > 0. Since borrower counties could differ along several dimension, we enrich our specifications with time-varying fixed effects at the county level. These fixed effects absorb unobservable county characteristics, for example loan demand. With countyxyear fixed effects, we essentially compare small business lending by two banks that differ in their IT intensity to borrowers in the same county, mitigating concerns that the relation between bank lending and house prices is due to (unobservable) confounding local factors, such as employment growth.

Table A5 shows that small business lending is more responsive to changes in local house prices for high-IT banks. To begin, column (1) illustrates that high-IT banks have higher small business lending growth on average, and that loan growth for the average bank is higher in counties with stronger house price growth. Columns (2) and (3) split the sample into banks with a low value of IT (bottom tercile of the distribution) and a high value (top tercile). A rise in house prices is associated with faster loan growth among high IT banks: The coefficient of house price’s growth is about 50% larger for the high-IT sample.

Columns (4)-(7) confirm the larger responsiveness of high-IT banks when we interact banks’ IT adoption with the change in house prices, using a set of increasingly saturated specifications. In column (4), small business lending reacts by significantly more to a change in house prices for banks with higher IT adoption. This finding is conditional on bank and county controls as well as year fixed effects to account for common trends. To further account for unobservable time-varying changes in unobservables across counties, we include county x year fixed effects in column (5). Despite a fourfold increase in the R-squared, estimated coefficients remain similar (the coefficient on the change in house prices is now absorbed). Column (6) further absorb time-invariant factors at the bank-county level (e.g. bank-borrower distance) and shows that the size of the coefficient of interest increases when we exploit within bank-county variation only. The coefficient on IT is now absorbed. Finally, column (7) controls for time-varying bank fundamentals through bankx year fixed effects. Essentially, comparing loan supply by the same bank to the same county for different levels of IT, we find that high-IT banks adjust their loan supply by more than low-IT banks when local house prices rise.

One caveat of CRA data is that it covers lending to small firms. While the vast majority of young firms are small, not all small firms are young. Despite this limitation, results in Table A5 are consistent with the model’s predictions that IT in banking increase the benefits of a rise in collateral values. Note that an additional benefit of these bank-county level regressions is that the measure of IT – which varies at the bank level – differs from the previously used measure of county exposure. Yet, under both measures we find similar results.

County IT exposure and Entrepreneurship-Differences

Secured Loans and Bank IT adoption

The role of local competition

Banks’ IT, house prices and home equity loans

Share of Loans in County with a Branch by Bank

Share of Loans Secured

The most recent draft is available here. We are grateful to seminar participants at FIRS, the 2nd DC Junior Finance Conference, IMF, International Network for Economic Research, EFiC 2021 Conference in Banking and Corporate Finance, The Future of Growth Conference, University of Bonn, and University of Halle, as well as Nigel Chalk, Daniil Kashkarov (discussant), Davide Malacrino, Ralf Meisenzahl (discussant), Mikhael Passo (discussant), Andrea Presbitero, Anke Weber, and Wei Xiong for their insightful comments. We thank Chenxu Fu for excellent research assistance. The views expressed in the paper are those of the authors and do not necessarily represent the views of the Bank of Canada, the Bank for International Settlements, nor of the IMF, its Executive Board, or its Management.

For instance, according to the 2007 Survey of Business Owners, the share of business owners who received initial financing through bank loans is more than ten times higher than that of owners who relied on venture capital.

The absence of major financial regulatory changes during our sample period makes it well-suited to identify the effects of IT on bank entrepreneurship. In fact, the period after the GFC is characterized by substantial financial regulatory reform (such as the Dodd-Frank Act and regular stress tests) and encompassing government programs, both of which have affected banks’ lending decisions, especially to small firms. A further reason to exclude the GFC and following years from the analysis is that during the crisis IT adoption determined the performance of mortgages originated by banks ( Pierri and Timmer, 2020 ), thus creating an important potential confounding factor. Also, detailed data on local entrepreneurship are unavailable before 1999, making it difficult to extend the analysis back in time.

Later waves of the same data set provide additional information on IT-budget and adoption of Cloud Computing at the establishment level: the number of PCs per employee is a strong predictor of these other measures of IT adoption in 2016. For example, the bank-level correlation between the per capita share of PCs and the IT budget is 65%. The measure has also been shown to be a valid proxy in the non-financial sector, for instance to predict firm productivity or local wage growth ( Bresnahan et al., 2002 ; Beaudry et al., 2010 ; Bloom et al., 2012 ).

The results are robust to other definitions of entrepreneurship.

DeYoung et al. (2008) show that the distance between borrowers and lenders increased over recent years. For a summary, see also Boot (2016) . Petersen (1999) ; Berger and Udell (2002) ; Hauswald and Marquez (2006) provide theoretical motivation and evidence on when and why banks rely on hard information, and how distance affects the decision.

We also relate to the literature on firm dynamics and the macroeconomy. While the slowdown in productivity after the Great Financial Crisis has been attributed to a large extent to frictions in the financial sector, see e.g. Doerr et al. (2018) ; Manaresi and Pierri (2019) ; Duval et al. (2020) , the impact of changes in the financial sector on firm dynamics before the crisis, especially in terms of IT, has received less attention.

For simplicity, we assume that these fixed costs are independent of the bank’s type. Our results can be generalized as long as the high-IT bank has a comparative advantage in screening via collateral.

For simplicity, we assume that these costs are independent of firm age.

When the bank has adopted IT, its cost of lending is 1 + v HighIT and the surplus from lending is p G x − (1 + v HighIT ). Since the bank keeps a fraction θ of this surplus, the equilibrium lending rate is r H i g h I T * = θ p G x + ( 1 − θ ) ( 1 + υ H i g h I T ) .

where PCs/Emp i,t is the ratio of computers per employee in branch i survey wave t (capped at top 1%), I T ˜ b is a bank fixed effect, θ type is a establishment-type (HQ, standalone, branch) fixed effects, θ c is a county fixed effect, θ t is a year fixed effect and Emp is the log number of employees in the establishment.

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Home » Business Finance » Role of Information Technology (IT) in the Banking Sector

Role of Information Technology (IT) in the Banking Sector

Banking environment has become highly competitive today. To be able to survive and grow in the changing market environment banks are going for the latest technologies, which is being perceived as an ‘enabling resource’ that can help in developing learner and more flexible structure that can respond quickly to the dynamics of a fast changing market scenario. It is also viewed as an instrument of cost reduction and effective communication with people and institutions associated with the banking business.

The Software Packages for Banking Applications in India had their beginnings in the middle of 80s, when the Banks started computerising the branches in a limited manner. The early 90s saw the plummeting hardware prices and advent of cheap and inexpensive but high powered PC’s and Services and banks went in for what was called Total Branch Automation (TBA) packages. The middle and late 90s witnessed the tornado of financial reforms, deregulation globalisation etc. coupled with rapid revolution in communication technologies and evolution of novel concept of convergence of communication technologies, like internet, mobile/cell phones etc. Technology has continuously played on important role in the working of banking institutions and the services provided by them. Safekeeping of public money, transfer of money, issuing drafts, exploring investment opportunities and lending drafts, exploring investment being provided.

Information Technology enables sophisticated product development, better market infrastructure, implementation of reliable techniques for control of risks and helps the financial intermediaries to reach geographically distant and diversified markets. Internet has significantly influenced delivery channels of the banks. Internet has emerged as an important medium for delivery of banking products and services.

The customers can view the accounts; get account statements, transfer funds and purchase drafts by just punching on few keys. The smart card’s i.e., cards with micro processor chip have added new dimension to the scenario. An introduction of ‘Cyber Cash’ the exchange of cash takes place entirely through ‘Cyber-books’. Collection of Electricity bills and telephone bills has become easy. The upgradeability and flexibility of internet technology after unprecedented opportunities for the banks to reach out to its customers. No doubt banking services have undergone drastic changes and so also the expectation of customers from the banks has increased greater.

IT is increasingly moving from a back office function to a prime assistant in increasing the value of a bank over time. IT does so by maximizing banks of pro-active measures such as strengthening and standardising banks infrastructure in respect of security, communication and networking, achieving inter branch connectivity, moving towards Real Time gross settlement (RTGS) environment the forecasting of liquidity by building real time databases, use of Magnetic Ink Character Recognition and Imaging technology for cheque clearing to name a few. Indian banks are going for the retail banking in a big way

The key driver to charge has largely been the increasing sophistication in technology and the growing popularity of the Internet. The shift from traditional banking to e-banking is changing customer’s expectations.

E-banking made its debut in UK and USA 1920s. It becomes prominently popular during 1960, through electronic funds transfer and credit cards. The concept of web-based baking came into existence in Eutope and USA in the beginning of 1980.

In India e-banking is of recent origin. The traditional model for growth has been through branch banking. Only in the early 1990s has there been a start in the non-branch banking services. The new pribate sector banks and the foreign banks are handicapped by the lack of a strong branch network in comparison with the public sector banks. In the absence of such networks, the market place has been the emergence of a lot of innovative services by these players through direct distribution strategies of non-branch delivery. All these banks are using home banking as a key “pull’ factor to remove customers away from the well entered public sector banks.

Many banks have modernized their services with the facilities of computer and electronic equipments. The electronics revolution has made it possible to provide ease and flexibility in banking operations to the benefit of the customer. The e-banking has made the customer say good-bye to huge account registers and large paper bank accounts. The e-banks, which may call as easy bank offers the following services to its customers:

• Credit Cards/Debit Cards
• EFT (Electronic Funds Transfer)
• DeMAT Accounts
• Mobile Banking
• Telephone Banking
• Internet Banking
• EDI (Electronic Data Interchange)

Benefits of E-banking:

To the Customer:

• Anywhere Banking no matter wherever the customer is in the world. Balance enquiry, request for services, issuing instructions etc., from anywhere in the world is possible.
• Anytime Banking — Managing funds in real time and most importantly, 24 hours a day, 7days a week.
• Convenience acts as a tremendous psychological benefit all the time.
• Brings down “Cost of Banking” to the customer over a period a period of time.
• Cash withdrawal from any branch / ATM
• On-line purchase of goods and services including online payment for the same.

To the Bank:

• Innovative, scheme, addresses competition and present the bank as technology driven in the banking sector market
• Reduces customer visits to the branch and thereby human intervention
• Inter-branch reconciliation is immediate thereby reducing chances of fraud and misappropriation
• On-line banking is an effective medium of promotion of various schemes of the bank, a marketing tool indeed.
• Integrated customer data paves way for individualised and customised services.

Impact of IT on the Service Quality:

The most visible impact of technology is reflected in the way the banks respond strategically for making its effective use for efficient service delivery. This impact on service quality can be summed up as below:

• With automation, service no longer remains a marketing edge with the large banks only. Small and relatively new banks with limited network of branches become better placed to compete with the established banks, by integrating IT in their operations.
• The technology has commoditising some of the financial services. Therefore the banks cannot take a lifetime relationship with the customers as granted and they have to work continuously to foster this relationship and retain customer loyalty.
• The technology on one hand serves as a powerful tool for customer servicing, on the other hand, it itself results in depersonalising of the banking services. This has an adverse effect on relationship banking. A decade of computerization can probably never substitute a simple or a warm handshake.
• In order to reduce service delivery cost, banks need to automate routine customer inquiries through self-service channels. To do this they need to invest in call centers, kiosks, ATM’s and Internet Banking today require IT infrastructure integrated with their business strategy to be customer centric.

Impact of IT on Banking System:

The banking system is slowly shifting from the Traditional Banking towards relationship banking. Traditionally the relationship between the bank and its customers has been on a one-to-one level via the branch network. This was put into operation with clearing and decision making responsibilities concentrated at the individual branch level. The head office had responsibility for the overall clearing network, the size of the branch network and the training of staff in the branch network. The bank monitored the organisation’s performance and set the decision making parameters, but the information available to both branch staff and their customers was limited to one geographical location.

Traditional Banking Sector

The modern bank cannot rely on its branch network alone. Customers are now demanding new, more convenient, delivery systems, and services such as Internet banking have a dual role to the customer. They provide traditional banking services, but additionally offer much greater access to information on their account status and on the bank’s many other services. To do this banks have to create account information layers, which can be accessed both by the bank staff as well as by th customers themselves.

The use of interactive electronic links via the Internet could go a ling way in providing the customers with greater level of information about both their own financial situation and about the services offered by the bank.

The New Relationship Oriented Bank

Impact of IT on Privacy and Confidentiality of Data:

Data being stored in the computers, is now being displayed when required on through internet banking mobile banking, ATM’s etc. all this has given rise to the issues of privacy and confidentially of data are:

• The data processing capabilities of the computer, particularly the rapid throughput, integration, and retrieval capabilities, give rise to doubts in the minds of individuals as to whether the privacy of the individuals is being eroded.
• So long as the individual data items are available only to those directly concerned, everything seems to be in proper place, but the incidence of data being cross referenced to create detailed individual dossiers gives rise to privacy problems.
• Customers feel threatened about the inadequacy of privacy being maintained by the banks with regard to their transactions and link at computerised systems with suspicion.

Aside from any constitutional aspect, many nations deem privacy to be a subject of human right and consider it to be the responsibility of those who concerned with computer data processing for ensuring that the computer use does not revolve to the stage where different data about people can be collected, integrated and retrieved quickly. Another important responsibility is to ensure the data is used only for the purpose intended.

Related posts:

• Article on Indian Banking Sector: “The challenges that the banking sector in India faces”
• Indian Banking Sector Reforms: Licensing of New Banks in the Private Sector
• Article on Indian Banking Sector: “Gobalization of International Banking”
• Article on Indian Banking Sector- “Innovation in Banking”
• Recent Trends in Indian Banking Sector
• Innovations in Customer Services in Banking Sector
• Customer Service Strategies in Banking Sector
• Indian Banking Sector Reforms: Disclosure Norms
• Strains and Challenges faced by Indian Banking Sector
• Narasimham Committee on Banking Sector Reforms (1998)

5 thoughts on “ Role of Information Technology (IT) in the Banking Sector ”

Good post, thanks for sharing. Any more information on DMA best practices? Thanks. Antonio

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tell me the role of IT act in Banking sector

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Role of Information Technology in Banking Sector with Special Reference to State Bank of India

2022, Recent Trends in Management and Commerce

Liberalization and information technology have attracted many foreign banks to India, Opening up new markets, new products, and efficient delivery avenues for the banking sector. The banking sector plays an important role in the growth of the Indian economy. Increased penetration, productivity, and efficiency through the use of technology. This not only increased the cost-effectiveness but also helped to process small value transactions. It improves choices and creates new markets and improves productivity and efficiency. It is observed that financial markets in India have become a market for buyers. Commercial banks in India are now becoming supermarkets in one place with the introduction of value-added and customized products, the focus shifts from mass banking to class banking. Technology Banks do not hire people for manual operations Allows you to create a branch in the lobby of the commercial building. Tele Banking, ATMs, Internet Banking, Mobile Banking, and branches through e-banking operate on a 24 X 7 operating principle. These technology-based delivery channels at a low cost are used to reach maximum customers very efficiently. The beauty of these banking innovations is that they put both the banker and the customer in a successful environment. Efficient use of technology has many times the effect on growth and development.

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International Journal of Business Analytics and Intelligence

Publishing India Group

Banks and other financial organisations have embraced the use of financial technology in their dayto-day operations. This has encouraged efficiency in banking operations, enabling cost reductions through the use of the Internet and electronic payments, among other things. The quality of services provided by banks around the world has consequently improved. Evidence demonstrates that development professionals consistently believe that the outreach brought about by Fintech will improve banks' financial performance. The independent variable for the study was financial technology operationalised as service banking, mobile banking, Internet banking, and ATMs. The control variables were economic growth represented by economic growth rate, exchange rate represented by the Indian Rupee, and interest rates measured as the average bank lending rate on a quarterly basis. The dependent variable was financial performance of the banking industry scaled as ROA. A period of five years, between April 2018 and March 2022, was studied through the gathering of secondary data. Descriptive research design was assumed, while multiple linear regressions model was applicable in the analysis of the relationship between the variables. The data was analysed by the application of SPSS version 23. The study constitutes that relinquishment of mobile banking, online or Internet banking, and agency banking impacted the financial performance of banks positively. The study concluded that banks should invest in financial technology to upgrade their operational efficiency and persuasiveness. The study also recommends that banks should mobilise their customers to adopt these technologies, as well as channel resources for the enhancement of technological infrastructure. Banks should also adopt further agency networks, as this widens their customer base, especially in the rural areas. It is the recommendation of this study that measures ought to be adopted that will enhance Internet banking, economic growth rate, and interest rate, as these measures have a substantial influence on the performance of the banking industry in India.

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

Banks are essential for converting citizen savings into investments. A solid banking system is essential for establishing sustainable growth of a nation. Banks conduct a variety of agency tasks, including providing financial advice, selling insurance and investment goods, and more, in addition to accepting deposits and disbursing loans. Technology is essential to the effective operation of banks and other financial organisations. These institutions' online and mobile banking platforms have greatly improved. The application of artificial intelligence and the digitalization of financial services have changed the entire banking experience, enabling institutions to reduce risks and expenses while increasing client happiness. The paper looks at the trends and development in the banking environment of India. I.

Dr.Akhil goyal

In this paper an attempt has been made to identify the general sentiments, challenges and Opportunities and recent IT Trends. For the Indian Banking Industry. This article is divided into some sections. Includes the introduction and general scenario of Indian banking industry. The second part discusses the various challenges and opportunities faced by Indian banking industry. Third part concludes that urgent emphasis is required on the Indian banking product and marketing strategies in order to get sustainable competitive edge over the intense competition from national and global banks. One of the recent trends of Indian banking sector to encourage Operational self-sufficiency, flexibility and competition in the system and to increase the banking standards in India to the international best practices. The main aim to Indian banking reorganization measures, human capital development, technological up-gradation, structural development which helped them for achieving universal benchmarks in terms of prudential norms and pre-eminent practices. This article is a small seed to existing branch of knowledge in banking industry and is useful for Bankers, strategist, policy makers and researchers and the impact of various market and regulatory initiatives on efficiency improvements of Indian banks. Efficiency of firm is measured in terms of its relative performance that is efficiency of a firm relative to the efficiencies of firms in a sample.

PRADEEP MULLEKYAL DEVADASAN

Financial institutions are the backbone of the Indian economy. Since economic liberalization after 1990, the Indian banking sector has witnessed growth along with remarkable improvement in its quality of assets and efficiency. Information Technology has become one's way of life in today's world that, it is difficult to imagine a world without IT. Technology which facilitates handling increased volumes at higher levels of efficiency. Hence, there is an imperative need for not mere technology up gradation but also integration of technology with the general way of functioning of banks. The Banking sector is no exception to this changing scenario which is sweeping across the world. Technology has given birth to a new era in banking. Indian banks are continuously encouraging the investment in information technology through ATMs, Net-banking, Mobile and Tele-banking, Automation of the banks, increasing use of plastic money, and the establishment of call centers. Nowadays banks are moving from disbursed operations to a centralized environment, powered by Information Technology. Banks are using new tools and techniques to reach better to its customers by offering tailor made products and Services. The changes in the banking landscape facilitated banks to compete in the new environment. Banks of the future will be a user friendly enterprise with technology aiming to achieve sustainable and valued business status. Information Technology has been imbibed in the banking operations with a vision to provide “Anytime Anywhere Banking” with customized services. In this paper we have discussed and analyzed the Changing landscape of financial Services in Indian Banking System in terms opportunities and challenges of technological developments, legal regulatory framework, and risk management.

Upendra Sastry S

Existence of a sound financial system with Banks constituting an integral part of it is a pre-requisite for any country to prosper, without India being an exception to this. As repositories of deposits and purveyors of credit, Banks play a strategic role in the sustainable socioeconomic development of our country. Though , the Banks in our country have initially started their journey by choosing the path of serving the banking needs of only the affordable and affluent sections and continued such system during the PreIndependence Period, like other sectors of our economy, even Banking Sector is also expected to be responsive to the needs of the disadvantaged sections, to enable the Union Government to accomplish the goals of the socialistic pattern of societythe path chosen by the Government after the attainment of Independence. As the Social Banking and Mass Banking have become the crying needs for the Government, to make weaker sections and vulnerable groups enjoy access to the for...

Malyadri Pacha

For an emerging economy like India, fast economic development is possible essentially by embracing ICT in a big way, as ICT alone can provide the requisite competitive edge that is essential for fast and sustained economic growth in a globalized environment. In its endeavor to become the ‘global services hub’, what India needs to attain is nothing but unmatched performance in respect of ICT. In this context, it is relevant to present the paper on a study of the Information Communication Technology (ICT) in the Indian Banking Industry. The researcher carried out this study to understand the overview of ICT in the Indian Banking Industry .The banking industry has undergone a major change in recent years. Global competition has forced the industry to be more agile and customer focused in all its services. Banks can no longer function in isolation, but have to operate cutting across physical boundaries. This has enthused software architects to develop suitable software development paradigms that can seamlessly integrate business functions across organizational boundaries. ICTs have been valuable in providing broader development benefits to all Indian citizens.. Banking across the world has undergone extensive changes, thanks to the profound influence of developments and trends in information communication technologies and the emerging technologies have made banking operations easier and more convenient. This paper presents the tremendous influence of information and communication technologies on banking. The paper concludes by saying that the banking technology discipline is all set for rapid. Key words: : Information Communication Technology (ICT), National Electronic Fund Transfer (NEFT), Automatic Teller Machine (ATM), Electronic Clearing System (ECS), Electronic Fund Transfer (EFT)

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Role Of IT In Banking Information Technology Essay

Published Date: 23 Mar 2015

Disclaimer: This essay has been written and submitted by students and is not an example of our work. Please click this link to view samples of our professional work witten by our professional essay writers . Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of EssayCompany.

In the five decades since independence, banking in India has evolved through four distinct phases. During Fourth phase, also called as Reform Phase, Recommendations of the Narasimham Committee (1991) paved the way for the reform phase in the banking. Important initiatives with regard to the reform of the banking system were taken in this phase. Important among these have been introduction of new accounting and prudential norms relating to income recognition, provisioning and capital adequacy, deregulation of interest rates & easing of norms for entry in the field of banking.

Entry of new banks resulted in a paradigm shift in the ways of banking in India. The growing competition, growing expectations led to increased awareness amongst banks on the role and importance of technology in banking. The arrival of foreign and private banks with their superior state-of-the-art technology-based services pushed Indian Banks also to follow suit by going in for the latest technologies so as to meet the threat of competition and retain their customer base.

Indian banking industry, today is in the midst of an IT revolution. A combination of regulatory and competitive reasons, have led to increasing importance of total banking automation in the Indian Banking Industry.

ROLE OF TECHNOLOGY

Information Technology has basically been used under two different avenues in Banking. One is Communication and Connectivity and other is Business Process Reengineering. Information technology enables sophisticated product development, better market infrastructure, implementation of reliable techniques for control of risks and helps the financial intermediaries to reach geographically distant and diversified markets.

In view of this, technology has changed the contours of three major functions performed by banks, i.e., access to liquidity, transformation of assets and monitoring of risks. Further, Information technology and the communication networking systems have a crucial bearing on the efficiency of money, capital and foreign exchange markets.

Internet has significantly influenced delivery channels of the banks. Internet has emerged as an important medium for delivery of banking products & services. Detailed guidelines of RBI for Internet Banking has prepared the necessary ground for growth of Internet Banking in India.

The Information Technology Act, 2000 has given legal recognition to creation, trans-mission and retention of an electronic (or magnetic) data to be treated as valid proof in a court of law, except in those areas, which continue to be governed by the provisions of the Negotiable Instruments Act, 1881.

As stated in RBI's Annual Monetary and Credit Policy 2002-2003: "To reap the full benefits of such electronic message transfers, it is necessary that banks bestow sufficient attention on the computerization and networking of the branches situated at commercially important centres on a time-bound basis. Intra-city and intra-bank networking would facilitate in addressing the "last mile" problem which would in turn result in quick and efficient funds transfers across the country".

TECHNOLOGY PRODUCTS IN A BANKING SECTOR:

Net Banking

Credit Card Online

Instant Alerts

Mobile Banking

e-Monies Electronic Fund Transfer

Online Payment of Excise & Service Tax

Phone Banking

Bill Payment

Ticket Booking

Railway Ticket Booking through SMS

Prepaid Mobile Recharge

Smart Money Order

Card to Card Funds Transfer

Funds Transfer (eCheques)

Anywhere Banking

Internet Banking

Bank @ Home "Express Delivery"

USE OF Technology in Banks to:

Have your paycheck deposited directly into your bank or credit union checking account.

Withdraw money from your checking account from an ATM machine with a personal identification number (PIN), at your convenience, day or night.

Instruct your bank or credit union to automatically pay certain monthly bills from your account, such as your auto loan or your mortgage payment.

Have the bank or credit union transfer funds each month from your checking account to your mutual fund account.

Have your government social security benefits check or your tax refund deposited directly into your checking account.

Buy groceries, gasoline and other purchases at the point-of-sale, using a check card rather than cash, credit or a personal check.

Use a smart card with a prepaid amount of money embedded in it for use instead of cash at a pay phone, expressway road toll, or on college campuses at the library's photocopy machine or bookstores.

Use your computer and personal finance software to coordinate your total personal financial management process, integrating data and activities related to your income, spending, saving, investing, recordkeeping, bill-paying and taxes, along with basic financial analysis and decision making.

TECHNOLOGY IS USED THROUGH:

Internet banking:.

Internet Banking lets you handle many banking transactions via your personal computer. For instance, you may use your computer to view your account balance, request transfers between accounts, and pay bills electronically.

Internet banking system and method in which a personal computer is connected by a network service provider directly to a host computer system of a bank such that customer service requests can be processed automatically without need for intervention by customer service representatives. The system is capable of distinguishing between those customer service requests which are capable of automated fulfillment and those requests which require handling by a customer service representative. The system is integrated with the host computer system of the bank so that the remote banking customer can access other automated services of the bank. The method of the invention includes the steps of inputting a customer banking request from among a menu of banking requests at a remote personnel computer; transmitting the banking requests to a host computer over a network; receiving the request at the host computer; identifying the type of customer banking request received; automatic logging of the service request, comparing the received request to a stored table of request types, each of the request types having an attribute to indicate whether the request type is capable of being fulfilled by a customer service representative or by an automated system; and, depending upon the attribute, directing the request either to a queue for handling by a customer service representative or to a queue for processing by an automated system.

AUTOMATED TELLER MACHINES (ATM):

An unattended electronic machine in a public place, connected to a data system and related equipment and activated by a bank customer to obtain cash withdrawals and other banking services. Also called automatic teller machine, cash machine; Also called money machine.

An automated teller machine or automatic teller machine (ATM) is an electronic computerized telecommunications device that allows a financial institution's customers to directly use a secure method of communication to access their bank accounts, order or make cash withdrawals (or cash advances using a credit card) and check their account balances without the need for a human bank teller (or cashier in the UK). Many ATMs also allow people to deposit cash or cheques, transfer money between their bank accounts, top up their mobile phones' pre-paid accounts or even buy postage stamps.

On most modern ATMs, the customer identifies him or herself by inserting a plastic card with a magnetic stripe or a plastic smartcard with a chip, that contains his or her account number. The customer then verifies their identity by entering a passcode, often referred to as a PIN (Personal Identification Number) of four or more digits. Upon successful entry of the PIN, the customer may perform a transaction.

If the number is entered incorrectly several times in a row (usually three attempts per card insertion), some ATMs will attempt retain the card as a security precaution to prevent an unauthorised user from discovering the PIN by guesswork. Captured cards are often destroyed if the ATM owner is not the card issuing bank, as non-customer's identities cannot be reliably confirmed.

The Indian market today has approximately more than 17,000 ATM's.

TELE BANKING:

Undertaking a host of banking related services including financial transactions from the convenience of customers chosen place anywhere across the GLOBE and any time of date and night has now been made possible by introducing on-line Telebanking services. By dialing the given Telebanking number through a landline or a mobile from anywhere, the customer can access his account and by following the user-friendly menu, entire banking can be done through Interactive Voice Response (IVR) system. With sufficient numbers of hunting lines made available, customer call will hardly fail. The system is bi-lingual and has following facilities offered

Automatic balance voice out for the default account.

Balance inquiry and transaction inquiry in all

Inquiry of all term deposit account

Statement of account by Fax, e-mail or ordinary mail.

Cheque book request

Stop payment which is on-line and instantaneous

Transfer of funds with CBS which is automatic and instantaneous

Utility Bill Payments

Renewal of term deposit which is automatic and instantaneous

Voice out of last five transactions.

SMART CARD:

A smart card usually contains an embedded 8-bit microprocessor (a kind of computer chip). The microprocessor is under a contact pad on one side of the card. Think of the microprocessor as replacing the usual magnetic stripe present on a credit card or debit card.

The microprocessor on the smart card is there for security. The host computer and card reader actually "talk" to the microprocessor. The microprocessor enforces access to the data on the card.

The chips in these cards are capable of many kinds of transactions. For example, a person could make purchases from their credit account, debit account or from a stored account value that's reload able. The enhanced memory and processing capacity of the smart card is many times that of traditional magnetic-stripe cards and can accommodate several different applications on a single card. It can also hold identification information, which means no more shuffling through cards in the wallet to find the right one -- the Smart Card will be the only one needed.

Smart cards can also be used with a smart card reader attachment to a personal computer to authenticate a user.

Smart cards are much more popular in Europe than in the U.S. In Europe the health insurance and banking industries use smart cards extensively. Every German citizen has a smart card for health insurance. Even though smart cards have been around in their modern form for at least a decade, they are just starting to take off in the U.S.

DEBIT CARD:

Debit cards are also known as check cards. Debit cards look like credit cards or ATM (automated teller machine) cards, but operate like cash or a personal check. Debit cards are different from credit cards. While a credit card is a way to "pay later," a debit card is a way to "pay now." When you use a debit card, your money is quickly deducted from your checking or savings account.

Debit cards are accepted at many locations, including grocery stores, retail stores, gasoline stations, and restaurants. You can use your card anywhere merchants display your card's brand name or logo. They offer an alternative to carrying a checkbook or cash.

An e-Cheque is the electronic version or representation of paper cheque.

The Information and Legal Framework on the E-Cheque is the same as that of the paper cheque's.

It can now be used in place of paper cheques to do any and all remote transactions.

An E-cheque work the same way a cheque does, the cheque writer "writes" the e-Cheque using one of many types of electronic devices and "gives" the e-Cheque to the payee electronically. The payee "deposits" the Electronic Cheque receives credit, and the payee's bank "clears" the e-Cheque to the paying bank. The paying bank validates the e-Cheque and then "charges" the check writer's account for the check

ADVANTAGES OF TECHNOLOGY

From both customer and banking perspectives it shows that the Internet is a convenience tool available whenever and wherever customers need it. It is also found that the Internet has improved the factors in service quality like responsiveness, communication and access. It is concluded that the Internet has an important and positive effect on customer perceived banking services and the service quality has been improved since the Internet has been used in banking sector.

It's generally secure. But make sure that the website you're using has a valid security certificate. This lets you know that the site is protected from cyber-thieves looking to steal your personal and financial information.

It gives twenty-four-hour access. When the neighborhood bank closes, you can still access your account and make transactions online. It's a very convenient alternative for those that can't get to the bank during normal hours because of their work schedule, health or any other reason.

It allows us to access our account from virtually anywhere. If we're on a business trip or vacationing away from home, we can still keep a watchful on our money and financial transactions - regardless of our location.

Conducting business online is generally faster than going to the bank. Long teller lines can be time-consuming, especially on a Pay Day. But online, there are no lines to contend with. You can access your account instantly and at your leisure.

Many features and services are typically available online. For example, with just a few clicks you can apply for loans, check the progress of your investments, review interest rates and gather other important information that may be spread out over several different brochures in the local bank.

Technology has opened up new markets, new products, new services and efficient delivery channels for the banking industry. Online electronics banking, mobile banking and internet banking are just a few examples.

Information Technology has also provided banking industry with the wherewithal to deal with the challenges the new economy poses. Information technology has been the cornerstone of recent financial sector reforms aimed at increasing the speed and reliability of financial operations and of initiatives to strengthen the banking sector.

The IT revolution has set the stage for unprecedented increase in financial activity across the globe. The progress of technology and the development of worldwide networks have significantly reduced the cost and time of global funds transfer.

It is information technology which enables banks in meeting such high expectations of the customers who are more demanding and are also more techno-savvy compared to their counterparts of the yester years. They demand instant, anytime and anywhere banking facilities.

IT has been providing solutions to banks to take care of their accounting and back office requirements. This has, however, now given way to large scale usage in services aimed at the customer of the banks.

IT also facilitates the introduction of new delivery channels--in the form of Automated Teller Machines, Net Banking, Mobile Banking and the like.

Use of de-mat account and online trading enables a person to buy and sell shares any time. The share trading companies and AMC's can give improved and faster service with help of technology.

There are many useful features and services available online besides for the usual transactions. For example, you can apply for credit cards, manage investments, and pay bills through your online account portal. You can also perform more mundane tasks such as ordering new checks, requesting additional deposit slips, or reporting a lost or stolen debit card.

Certainly the above mentioned advantages if technology have improved the quality of service in a banking and financial sector.

Price- In the long run a bank can save on money by not paying for tellers or for managing branches. Plus, it's cheaper to make transactions over the Internet by using technology.

Customer Base- The technology allows banks to reach a whole new market- and a well off one too, because there are no geographic boundaries with the help of Internet. The Internet also provides a level playing field for small banks who want to add to their customer base.

Efficiency- Banks can become more efficient than they already are by providing Internet access for their customers. With the use of technology the banks almost become paper less system.

Customer Service and Satisfaction- Banking on the Internet not only allow the customer to have a full range of services available to them but it also allows them some services not offered at any of the branches. The person does not have to go to a branch where that service may or may not be offer. A person can print of information, forms, and applications via the Internet and be able to search for information efficiently instead of waiting in line and asking a teller. With more better and faster options a bank will surly be able to create better customer relations and satisfaction.

Image- A bank seems more state of the art to a customer if they offer Internet access, e-banking, telebanking. A person may not want to use these but having the service available gives a person the feeling that their bank is on the cutting image.

For Customers:

Bill Pay: Bill Pay is a service offered through Internet banking, e-banking that allows the customer to set up bill payments to just about anyone. Customer can select the person or company whom he wants to make a payment and Bill Pay will withdraw the money from his account and send the payee a paper check or an electronic payment

Other Important Facilities: E- banking gives customer the control over nearly every aspect of managing his bank accounts. Besides the Customers can, Buy and Sell Securities, Check Stock Market Information, Check Currency Rates, Check Balances, See which checks are cleared, Transfer Money, View Transaction History and avoid going to an actual bank. The best benefit is that Internet banking is free. At many banks the customer doesn't have to maintain a required minimum balance. The second big benefit is better interest rates for the customer.

Benefits of technology platform

Banks adopting technologies see a number of other benefits, including:

Increased employee satisfaction Technology empowers bank employees to better serve customers. With quick and complete access to customer data, they spend less time searching for information and more time cross-selling and retaining customers.

Lower total cost of ownership Banking solutions built on the technology platform can be deployed faster than those based on competing infrastructures-and its solutions can cost less to manage over time. Training and other costs can be minimized if employees already know how to use required technology.

Greater return on investment The use of technology helps banks get more value out of legacy systems.

Reduced operations risk

Concerns with e-banking.

As with any new technology new problems are faced.

Customer support - banks will have to create a whole new customer relations department to help customers. Banks have to make sure that the customers receive assistance quickly if they need help. Any major problems or disastrous can destroy the banks reputation quickly an easily. By showing the customer that the Internet is reliable you are able to get the customer to trust online banking more and more.

Laws - While Internet banking does not have national or state boundaries, the law does. Companies will have to make sure that they have software in place software market, creating a monopoly.

Security: customer always worries about their protection and security or accuracy. There is always a question whether or not something took place.

Other challenges: lack of knowledge from customers end, sit changes by the banks, etc

INFORMATION TECHNOLOGY: A GLOBAL PERSPECTIVE

The advent of Internet has initiated an electronic revolution in the global banking sector. The dynamic and flexible nature of this communication channel as well as its ubiquitous reach has helped in leveraging a variety of banking activities. New banking intermediaries offering entirely new types of banking services have emerged as a result of innovative e-business models. The Internet has emerged as one of the major distribution channels of banking products and services, for the banks in US and in the European countries.

Initially, banks promoted their core capabilities i.e., products, services and advice through Internet. Then, they entered the e-commerce market as providers/distributors of their own products and services. More recently, due to advances in Internet security and the advent of relevant protocols, banks have discovered that they can play their primary role as financial intermediates and facilitators of complete commercial transactions via electronic networks especially through the Internet. Some banks have chosen a route of establishing a direct web presence while others have opted for either being an owner of financial services centric electronic marketplace or being participants of a non-financial services centric electronic marketplace.

The trend towards electronic delivery of banking products and services is occurring partly as a result of consumer demand and partly because of the increasing competitive environment in the global banking industry. The Internet has changed the customer's behaviors who are demanding more customized products/services at a lower price. Moreover, new competition from pure online banks has put the profitability of even established brick and mortar banks under pressure. However, very few banks have been successful in developing effective strategies for fully exploiting the opportunities offered by the Internet. For traditional banks to define what niche markets to serve and decide what products/services to offer there is a need for a clear and concise Internet commerce strategy.

Banking transactions had already started taking place through the Internet way back in 1995. The Introduction of technology promised an ideal platform for commercial exchange, helping banks to achieve new levels of efficiency in financial transactions by strengthening customer relationship, promoting price discovery and spend aggregation and increasing the reach. Electronic finance offered considerable opportunities for banks to expand their client base and rationalize their business while the customers received value in the form of savings in time and money.

Global E-banking industry is covered by the following four sections:

E-banking Scenario: It discusses the actual state, prospects, and issues related to E-banking in Asia with a focus on India, US and Europe. It also deals with the impact of E-banking on the banking industry structure.

E-banking Strategies: It reveals the key strategies that banks must implement to derive maximum value through the online channel. It also brings guidance for those banks, which are planning to build online businesses.

E-banking Transactions: It discusses how Internet has radically transformed banking transactions. The section focuses on cross border transactions, B2B transactions, electronic bill payment and presentment and mobile payments. In spite of all the hype, E-banking has been a non-starter in several countries.

E-banking Trends: It discusses the innovation of new technologies in banks.

E-BANKING SCENARIO:

The banking industry is expected to be a leading player in E-business. While the banks in developed countries are working primarily via Internet as non-branch banks, banks in the developing countries use the Internet as an information delivery tool to improve relationship with customers.

In early 2001, approximately 60 percent of E-business in UK was concentrated in the financial services sector, and with the expected 10-fold increase of the British E-business market by 2005, the share of the financial services will further increase. Around one fifth of Finish and Swedish bank customers are banking online, while in US, according to UNCTAD, online banking is growing at an annual rate of 60 percent and the number of online accounts has approximately reached 15 million by 2006.

In Asia, the major factor restricting growth of E-banking is security, in spite of several countries being well connected via Internet. Access to high-quality E-banking products is an issue as well. Majority of the banks in Asia are just offering basic services compared with those of developed countries. Still, E-banking seems to have a future in Asia. It is considered that E-banking will succeed if the basic features, especially bill payment, are handled well. Bill payment was the most popular feature, cited by 40 percent of respondents of the survey. However, providing this service would be difficult for banks in Asia because it requires a high level of security and involves arranging transactions with a variety of players.

In 2001, over 50 percent of the banks in the US were offering E-banking services. However, large banks appeared to have a clear advantage over small banks in the range of services they offered. Some banks in US were targeting their Internet strategies towards business customers. Apart from affecting the way customers received banking services; E-banking was expected to influence the banking industry structure. The economics of E-banking was expected to favor large banks because of economies of scale and scope, and the ability to advertise heavily. Moreover, E-banking offered entry and expansion opportunities that small banks traditionally lacked.

In Europe, the Internet is accelerating the reconfiguration of the banking industry into three separate businesses: production, distribution and advice. This reconfiguration is being further driven by the Technology, due to the combined impact of:

The emergence of new and more focused business models

New technological capabilities that reduces the banking relationship and transaction costs.

High degree of uncertainty over the impact that new entrants will have on current business models.

Though E-banking in Europe is still in the evolutionary stage, it is very clear that it is having a significant impact on traditional banking activities. Unlike in the US, though large banks in the Europe have a competitive edge due to their ability to invest heavily in new technologies, they are still not ready to embrace E-banking. Hence, medium-sized banks and start-ups have an important role to play on the E-banking front if they can take concrete measures quickly and effectively.

ROLE OF RBI IN COMPUTERIZATION OF BANKS IN INDIA

Computerization became popular in the western countries right from the Sixties. Main Frames were extensively used both by the Public Institutions and Major Private Organizations. In the Seventies Mini Computer became popular and Personal Computers in early Eighties, followed by introduction of several software products in high level language and simultaneous advancement in networking technology. This enabled the use of personal computers extensively in offices & commercial organisations for processing different kinds of data.

However in India organized Trade Unions were against introduction of computers in Public Offices. Computerization was restricted to major scientific research organizations and Technical Institutes and defense organizations. Indian Railways first accepted computerization for operational efficiency.

The Electronics Corporation of India Ltd. was set up in 1967 with the objective of research & development in the fields of Electronic Communication, Control, instrumentation, automation and Information Technology. CMC Ltd (Computer Maintenance Corporation of India Ltd.) was established in 1976 to look after maintenance operations of Main Frame Computers installed in several organizations in India, to serve the gap, when IBM left India, due to the directive of the then Central Government.

In the Private Sector the first major venture was TCS (Tata Consultancy Services) which started functioning from 1968. In the year 1980 a few batch-mates of IIT Delhi pioneered the effort to start a major education centre in India to impart training in Information Technology and their efforts resulted in the setting up of NIIT in 1981. Aptech Computer Education was established in 1986 following the experiment of NIIT.

Before large scale computerization, computer education became popular in India and coveted by bright students, when several Engineering Colleges and Technical Institutes introducing Post Graduate Degree courses in Computer Engineering. The booming hardware and software industry in the West attracted Indian students and many of them migrated for better opportunities to the U.S.A. and settled there. We have today the paradox of India being one of the major powers possessing diverse talents in fields of software development, but at the same time, we are still a decade back to the using computerized service extensively in the country and bringing the facility to the realms of the common man.

Rapid development of business and industry brought manual operations of data, a saturation point. This acted as a overload on the growing banking operations. Government owned banks in general found the "house-keeping" unmanageable. Several heads of accounts in particular inter-bank clearing and inter-branch reconciliation of accounts went totally out of control.

Low productivity pushed cost of wages high and employees realized that unless they agreed for computerization further improvement in their wage structure was not possible.

In the year 1993, the Employees' Unions of Banks signed an agreement with Bank Managements under the auspices of Indian Banks' Association (IBA). This agreement was a major break through in the introduction of computerized applications and development of communication networks in Banks.

The first initiatives in the area of bank computerization, however, stemmed out of the landmark report of the two committees headed by the former Governor of the Reserve Bank of India and currently Governor of Andhra Pradesh, His Excellency, Dr.C.Rangarajan. Both the reports had strongly recommended computerization of banking operations at various levels and suggested appropriate architecture.

In the 'seventies, there was a four-fold increase in the number of branches, five-fold increase in advances and a six-fold increase in deposits'. Mechanization was seen as the best solution to the "problems inherent in the manual system of operations, their adverse impact on customer services and the grave dangers to banks in the context of increasing incidence of frauds.

The first of these Committees, viz. the Committee on the Mechanization of the Banking Industry (1984) was set up for the first time to suggest a model for mechanization of bank branches, regional / controlling offices and Head Office necessitated by the explosive growth in the geographical spread of banking following nationalization of banks in 1969.

In the first phase of computerization spanning the five years ending 1989, banks in India had installed 4776 ALPMs at the branch level, 233 mini computers at the Regional/Controlling office levels and trained over 2000 programmers/systems personnel and over 12000 Data Entry Terminal Operators. The Reserve Bank too had embarked upon an ambitious program to bring about state-of-the-art technology in the clearing process and had introduced MICR clearing at 4 centres and computerized clearing settlement at 9 centres.

Against this backdrop, the Committee on Computerisation in Banks was set up once again under Dr.Rangarajan's Chairmanship to draw up a perspective plan for computerisation in banks. In its report submitted in 1989, the Committee acknowledged the gains of the initial efforts and sought to move away from the stand-alone dedicated systems to an on-line transaction processing environment in branch banking. It recommended that the thrust of bank computerisation for the following 5 years should be to fully computerise the operations at both the front and back offices of large branches then numbering around 2500.

RECOMMENDATIONS OF COMMITTEE ON TECHNOLOGY UPGRADATION

The Reserve Bank continued to be involved in shaping the technology vision of the banking system. Following the recommendations of the Committee on Financial Sector Reforms, (which is popularly known as the second Narasimham committee), a Committee on Technology Upgradation was set up by the RBI for the Banking Sector in 1994. This committee has representation from banks, Government, technical institutions and the RBI. Among other things, this committee looked into issues relating to

Encryption of Public Switching Telephone Network (PSTN) lines

Admission of electronic files as evidence

Record keeping

Modalities for a satellite based WAN for banks and financial institutions with the necessary security systems by banks and other financial institutions, to ultimately develop a sound and an efficient payments system

Methods by which technological upgradation in banks and financial institutions could be effected and in the context study the feasibility of establishment of standards, designing payments system backbone and standards relating to security levels, messages and smart cards.

The Committee realised the urgent need for training, research and development activities in the Banking Technology area. Banks and Financial Institutions started setting up Technology based training centres and colleges. However, a need was felt for an apex level Institute which could be a Think-tank and Brain Trust for Banking Technology.

The committee recommended a variety of payment applications which can be implemented with appropriate technology upgradation and development of a reliable communication network. The committee also suggested setting up of an Information Technology Institute for the purpose of Research and Development as well as Consultancy in the application of technology to the Banking and Financial sector of the country. As recommended by the Committee, IDRBT was established by RBI in 1996 as an autonomous centre for Development and Research in Banking Technology at Hyderabad.

LOOKING FORWARD

An old Chinese saying goes: If you don't know where you are going - you will never get there. Globally, the financial sector is metamorphosing under the impact of competitive, regulatory and technological forces. The banking sector is currently in a transition phase with re-alignment, mergers and entry of new players from different industry is becoming common. Many countries including India are de-regulating their banking sector and government policies no longer form an entry barrier to banks competitors. ICICI Bank, IDBI Bank, HDFC Bank and recently Kotak Mahindra Bank are prime examples of these.

Technology has leveled the playing field: the bargaining power of consumers is increasing, switching costs are becoming lower and consumer loyalties are harder to retain. Primary goal of the banking sector including every Bank is mainly to make profit, which in turn is ploughed back to increase business and reach, and pay dividends or share profits to the stakeholders. This is perfectly correct, yet generic goal. More over the product (schemes) differentiation is very difficult for banks as most of the products sold are constrained by legal or industry regulations. Now, if you are already thinking about Technology as a tool in Banking you could probably set some of these goals:

Selling financial products and services

Cutting operational costs

Branding & Market recognition

Keeping profitable customers

Every day more and more people are turning to the Technology for their personal banking. It is a safe, convenient way to shop for financial services, maintain bank accounts and conduct business 24 hours a day. Every one of us has always enjoyed a special relationship with their neighborhood bank. Why are so many people suddenly choosing their personal computers as the new way to view and manage their money? Quite simple - because it is a valuable option to have. Bank customers can save time by banking online. There is no need to stand in one more line to perform the most basic transactions when they can be done quickly from the desktop PC anytime, day or night. But even with more complicated transactions or investment decisions, people like having direct control over their finances themselves. They find it convenient to access all of their financial information in one place. Ease of use is one of the most important factors. Navigation through online banking should be simple and intuitive. Banks need to appeal to customers who may not be technologically sophisticated, and should not require an engineering degree to get started or use the service. Customers also choose banks whose online services are reliable. Most Banks now offers a comprehensive range of financial products and services, including a FREE checking account and internet bill paying services. In addition, an array of checking accounts is available in which you may also request a FREE check card. Hence most Banks of following Electronic Banking or Internet Banking FREE have following services:

Get your balance details, Obtain your last 3 transaction details, Request a cheque book, Stop a cheque payment, Enquire cheque status, Request an account statement, Get Fixed Deposit details, Bill payment details for electricity, mobile phone and telephone services, Convenience of setting an operative account, Designate a particular account linked to your customer id as the operative account. Customer Service available 24 hours a day, 7 days a week E-banking Benefits

Benefits for the bank should always reflect benefits for the customer of banking services.

Review of literature

1. Offline and online banking - where to draw the line when building trust in e-banking?

Kenneth B. Yap, David H. Wong, Claire Loh, Randall Bak. The International Journal of Bank Marketing. Bradford: 2010. Vol. 28, Iss. 1; pg. 27

The purpose of this paper is to examine the role of situation normality cues (online attributes of the e-banking web site) and structural assurance cues (size and reputation of the bank, and quality of traditional service at the branch) in a consumer's evaluation of the trustworthiness of e-banking and subsequent adoption behaviour. Data were collected from a survey and a usable sample of 202 was obtained. Hierarchical moderated regression analysis was used to test the model. Traditional service quality builds customer trust in the e-banking service. The size and reputation of the bank were found to provide structural assurance to the customer but not in the absence of traditional service quality. Web site features that give customers confidence are significant situation normality cues. Bank managers have to realise that good service at the branch is a necessary condition for the promotion of e-banking. They cannot rely on bank size and reputation to "sell" e-banking. This is the first study that examines how traditional service quality and a bank's size and reputation influences trust in e-banking.

2. E-Banking makes financial transactions easer

The Financial Daily. Karachi: Apr 27, 2010. Vol. 3, Iss. 262

Muhammad Kamran Shahzad, Deputy Governor State Bank (SBP), in his address said that the banking sector is now more innovative by using the technology and banks are providing a variety of products and services to the customer and by e-Banking these products and services are available across the country.

3. Innovations in Indian Banking Sector

The caselet gives an overview of the innovative services offered by the banks in India to stay ahead of the competition. Most of these banks took the help of proprietary processes and technology to launch innovative products to woo customers and differentiate themselves from the competition. Banks also started using their ATMs as a means of differentiating their services, making them more accessible and attractive to consumers. They added bill payment and credit card payment options at the ATMs. In addition, the banks used service personnel as a means of differentiation.

4. Innovations in the Banking Industry in India

In the 1990s, the banking sector in India saw greater emphasis being placed on technology and innovation. Banks began to use technology to provide better quality of services at greater speed.

Internet banking and mobile banking made it convenient for customers to do their banking from geographically diverse places. Banks also sharpened their focus on rural markets and introduced a variety of services geared to the special needs of their rural customers.

Banking activities also transcended their traditional scope and new concepts like personal banking, retailing and banc assurance were introduced.

The sector was also moving rapidly towards universal banking and electronic transactions, which were expected to change the way banking would be perceived in the future.

5. Feeling Safe Online -- To keep consumers' trust and keep them banking online, try tokens and smart cards, analysts say.

Michael Singer, InformationWeek. Bank Systems & Technology. New York: Mar 2007. Vol. 44, Iss. 3; pg. 19

Banks and other financial institutions should adopt stronger authentication measures, such as security tokens and smart cards, if they want to increase customer satisfaction with Internet banking, according to a panel of industry analysts. The panel - which was comprised of security advisers from Yankee Group, TowerGroup and Gartner - was made available recently on a media call arranged by RSA, the Bedford, Mass.-based security division of EMC.

DISADVANTAGES OF TECHNOLOGY

Yes, online banking is generally secure, but it certainly isn't always secure. Identity theft is running rampant, and banks are by no means immune. And once your information is compromised, it can take months or even years to correct the damage, not to mention possibly costing you thousands of dollars, as well. This generally does not happen in case of traditional method of banking.

Some online banks are more stable than others. Not all online setups are an extension of a brick-and-mortar bank. Some operate completely in cyberspace, without the benefit of a branch that you can actually visit if need be. With no way to physically check out the operation, you must be sure to thoroughly do your homework about the bank's background before giving them any of your money.

Before using a banking site that you aren't familiar with, check to make sure that their deposits are FDIC-insured. If not, you could possibly lose all of your deposits if the bank goes under, or its major shareholders decide to take an extended vacation in Switzerland.

Customer service can be below the quality that you're used to. Some people simply take comfort in being able to talk to another human being face-to-face if they experience a problem. Although most major banks employ a dedicated customer service department specifically for online users, going through the dreaded telephone menu can still be quite irritating to many. Again, some are considerably better (or worse) than others.

Not all online transactions are immediate. Online banking is subject to the same business-day parameters as traditional banking. Therefore, printing out and keeping receipts is still very important, even when banking online.

If your bank operates only online or simply does not have a branch office in your local area, you will not be able to reach a representative in person for discussion of account issues. Normally this is not a problem, but sometimes customer service by telephone or email can be spotty and may prove to be more of a hassle if you have a serious issue that is not easily resolved. Some banks are better than others in this department, so you will need to do some research if this is an important consideration for you.

Using online banking effectively requires some basic computer literacy and familiarity with navigating the Internet. While this is not a problem for people like me, those who are afflicted with technophobia or are simply inexperienced with this particular genre may not be comfortable with this concept. There are also a significant number of people who are suspicious of anything having to do with the Internet because it is outside of their comfort zone. Others are simply too stubborn to acquire the relevant knowledge and skills.

Technology has been one of the most important factors for the development of mankind. Information and communication technology is the major advent in the field of technology which is used for access, process, storage and dissemination of information electronically. Banking industry is fast growing with the use of technology in the from of ATMs, on-line banking, Telephone banking, Mobile banking etc., plastic card is one of the banking products that cater to the needs of retail segment has seen its number grow in geometric progression in recent years. This growth has been strongly supported by the development of in the field of technology, without which this could not have been possible of course it will change our lifestyle in coming years.

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Home — Essay Samples — Information Science and Technology — Technology in Education — The Role of Technology in Education

The Role of Technology in Education

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Customer Perceptions of CSR and Customer Loyalty Towards Digital Banking in Vietnam - Mediating Role of Customer Satisfaction

16 Pages Posted: 26 Jun 2024

Thị Minh Duyên Lê

National Economic University

Date Written: May 28, 2024

This study examines the impact of Corporate Social Responsibility (CSR) perceptions on customer loyalty within the digital banking sector in Vietnam, focusing on the mediating role of customer satisfaction. Amidst the rapid advancement of digital banking and the increasing importance of CSR in contemporary business strategies, this research aims to explore how CSR initiatives influence customer satisfaction and, subsequently, loyalty in a developing country context. Utilizing a mixed-method approach and employing Partial Least Squares Structural Equation Modeling (PLS-SEM) on data collected from 510 respondents, the findings reveal that positive perceptions of CSR significantly enhance customer satisfaction, which in turn positively affects customer loyalty. This study contributes to the existing literature by highlighting the importance of CSR in the digital banking sector and providing empirical evidence from Vietnam. It offers theoretical insights into the CSR-customer loyalty nexus and presents practical implications for banking institutions on integrating CSR into their business strategies to enhance customer loyalty. The research underscores the strategic value of CSR in building and maintaining customer loyalty in the rapidly evolving digital banking landscape.

Keywords: Corporate Social Responsibility, Customer perception of CSR, Customer loyalty, Customer satisfaction, Digital Banking

Suggested Citation: Suggested Citation

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Simplify reporting with real-time insight into your sustainability progress

• By Shefy Manayil Kareem, General Manager, Microsoft Cloud for Sustainability
• Microsoft Industry Clouds
• Microsoft Sustainability Manager

In the landscape of corporate sustainability, the ability to develop and measure targets is crucial for organizations charting their environmental impact. Using the scorecards and goals feature in Microsoft Sustainability Manager , you can track sustainability metrics and get a clear view of your organization’s environmental pledges and business operations. 

Microsoft Sustainability Manager Premium

A solution that unifies data to help you monitor and manage your performance

In this blog, we explain how creating scorecards and goals empowers you to curate sustainability metrics and track against your organization’s key business objectives. We also share other recent updates to Microsoft Cloud for Sustainability designed to help you manage, track, report, and gain better insight into your sustainability data. 

Use scorecards and goals to achieve your sustainability objectives

Sustainability reporting can be a complex process. Sustainability Manager includes a new capability that simplifies the process, making it more accessible and manageable for you to communicate your organization’s sustainability achievements. With scorecards and goals , you can curate your organization’s sustainability metrics and track them against your organization’s business objectives. 

Create a scorecard , which you can use to chart your organization’s sustainability metrics, encapsulate them within a scorecard, and designate an owner to guide its advancement. 

Create goals , the benchmark of your organization’s sustainability trajectory, which can be seamlessly integrated with the scorecards. These goals, whether entered manually or derived from interconnected data streams, provide a dynamic framework for sustainability targets, adaptable to the shifting tides of your organization’s needs. 

Using the scorecards and goals feature, you can mark goals as aligning to the Science Based Targets initiative (SBTi) , giving your organization the ability to highlight the scientific rigor and global recognition of its sustainability efforts. By including a baseline year, the goal-setting process deepens, offering a historical perspective from which you can measure and assess progress. Finally, the dual capability of manual updates or automated system tracking enables you to not only set but actively pursue and achieve your organization’s sustainability goals.  

The scorecards and goals capability transcends the mere establishment of targets, fostering a culture of engagement and accountability. It provides a centralized platform for setting, monitoring, and updating sustainability goals, which is crucial for accurate disclosure reporting. With the ability to connect goals to data sources, you can ensure that your organization’s reporting is data-driven and reflects real-time progress towards its sustainability targets. This feature simplifies the complex process of sustainability reporting, making it more accessible and manageable for organizations of all sizes to communicate their sustainability achievements transparently to stakeholders.  

Calculate emissions using IEA factors within Sustainability Manager 

Multinational organizations can now calculate emissions using International Energy Agency (IEA) emission factors within Sustainability Manager, with some restrictions as governed by IEA . Using the IEA factors can help you understand your organization’s carbon footprint and develop strategies to reduce emissions, as well as help with regulatory compliance, risk management, and cost reduction. 

The IEA factors library is available to download from Microsoft Cloud Solution Center . The IEA emission factors are available to use alongside all other emission factor libraries within Sustainability Manager. This feature is available within Sustainability Manager Premium.

Learn how to calculate emissions with the IEA emissions feature . 

Create and associate meter entities with a facility within Sustainability Manager 

For Scope 1 and Scope 2 emission categories, customers collect utility consumption data from utility providers through bills and, in some cases, through real-time devices like meters. The consumption reported in utility bills and real-time devices are recorded through utility meters (per collection device configuration). 

Now deprecated, Sustainability Manager previously included an optional meter text attribute for purchased energy and stationary combustion. However, since the consumption is recorded at a facility level and the meter is the device used to record this consumption for each facility, the meter must be associated with a facility within Sustainability Manager. 

You can now create and associate multiple utility meters (entities) with the facility entity in Sustainability Manager. This will enable you to track and report energy consumption for emissions calculations, as well as water usage at a facility level within your organization. 

Note that reports by meter is a future enhancement. In the meantime, you can use custom reports to add meter-based pivot views. 

Transfer data connections across deployments 

Sustainability Manger includes advanced ingestion capabilities. A connection is made of several parts:   

• The data source connectivity specifications  
• Mode of ingestion such as Power Query, Excel, or from a custom data provider  
• Mapping of source shape to the Sustainability Manager data model  
• Execution context, such as scheduling  

Enterprises need repeatable, verifiable processes across deployments; so, once a connection is specified, it can be transferred across deployments or environments. Now, environment administrators can use application lifecycle management (ALM) with Microsoft Power Platform to transfer connections across environments.   

Learn more about how to copy connections from one environment to another . 

Gain insights into your organization’s sustainability progress with Microsoft Copilot Studio templates 

The Sustainability Insights Copilot template (preview) was recently added to Copilot Studio. The template enables you to get insights and see data about your organization’s sustainability goals and progress and can be tailored to suit your organization’s specific needs. Information can be publicly shared in the form of reports, documents, and records. For example, a company’s sales and marketing professionals might be required to respond to queries from customers about the company’s sustainability progress on various sustainability fronts like measuring across environmental metrics, social and governance stats and indexes, energy meters, pollution indexes, and biodiversity impact.  

You can create and deploy a Sustainability Insights Copilot template as you would for any other Copilot template in Copilot Studio. 

Once deployed, your copilot is ready to field questions.  

You can configure the template with more sources of knowledge and other system of records, leveraging connectors to respond to related queries better.  

Enhanced Scope 3 categories now generally available in Sustainability Manager 

We’re excited to announce that Sustainability Manager has successfully completed the verification process for several Scope 3 categories of the Greenhouse Gas (GHG) Protocol . The following categories are now generally available:  

• Category 3: Fuel-and energy-related activities  
• Category 10: Processing of sold products  
• Category 11: Use of sold products  
• Category 14: Franchises  
• Category 15: Investments 

This update spans across various features , including import, activity data, calculations, and documentation, ensuring a seamless user experience.  

The verification process was comprehensive, involving a rigorous assessment and validation of data, methodologies, and calculations related to these Scope 3 categories. Independent experts conducted a thorough review to ensure accuracy, consistency, and compliance with industry standards.  

We’re committed to providing you with reliable and transparent tools to manage your sustainability efforts. The general availability of these enhanced Scope 3 categories marks a significant milestone in our journey towards empowering organizations to achieve their environmental goals. 

Learn more about sustainability solutions with Microsoft 

• Bookmark Microsoft Industry Blogs: Sustainability for the latest updates, including new capabilities in Microsoft Cloud for Sustainability.  
• Discover solutions with  Microsoft Sustainability Manager . 
• Explore the Microsoft Cloud for Sustainability Community , where you can find answers to questions and connect with peers and experts. 
• See what’s new in Microsoft Cloud for Sustainability June 2024 .

Want to learn more about  Microsoft Cloud for Sustainability ? Sign up for  news and updates . 

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Election latest: Police to probe potential 'misconduct in public office' in betting scandal - after fiery final TV debate

The Metropolitan Police is taking the lead in a "small number of cases" relating to the scandal around bets on the date of the general election. Last night, Sunak and Starmer traded blows in their final TV debate of the election, as campaigning intensifies with just seven days to go.

Thursday 27 June 2024 09:31, UK

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Sir Keir Starmer has said repeatedly through the general election campaign that he will not be able to give the 35% pay rise the junior doctors are asking for because it is unaffordable.

In reaction to that, co-chair of the BMA junior doctors committee, Dr Rob Laurenson, told Sky News that the Labour leader "would do well not to repeat the mistakes of Rishi Sunak and to empower his health secretary to negotiate in good faith".

He said they have been listening to shadow health secretary Wes Streeting who has been "talking about a journey, not an event".

"We're very much happy to talk about how we get to pay restoration - we've always said all along that we'd be happy to have this in a multi-year pay deal and not all in one go."

But more broadly, he said junior doctors are currently paid £15 an hour, and they want an increase to £21 an hour, which he insisted is affordable.

"The government has spent £3bn on strikes, and pay restoration costs £1.3bn.

"So if an incoming government under Keir Starmer wants to continue lying, then it looks like strikes will have to continue as well."

Junior doctors are on strike today as part of their long-running dispute over pay, and we just spoke with Dr Rob Laurenson, co-chair of the BMA junior doctors committee.

We asked for his reaction to both parties criticising this industrial action, and he replied: "I'm not really surprised that no one's happy about this situation. We're not happy about this situation."

He said it has been four months since they last took industrial action, and they've "tried to negotiate in good faith during that time", but he blamed the government for a resolution not being reached.

Asked if he can understand why some may view the strikes as cynical given that a resolution will not be reached during the election campaign, he replied: "Not at all, and this is precisely why the government is lying to everyone."

He said their ask during this set of strikes is for a "credible commitment" from Rishi Sunak - as with any other manifesto commitment - to restoring their pay to 2010 levels.

"Rishi Sunak's made a big point about making clear plans and giving bold action. But the truth of the matter is he has no plan."

Dr Laurenson called on the PM to "sit down, drop the rhetoric, talk about pay", otherwise the strikes will have to continue.

It was reported yesterday that Labour's Darren Jones told a meeting in his constituency that the party's net zero plans would cost "hundreds of billions" of pounds.

Mr Jones told Sky News after the debate that the vast majority of that money would come from private investment, and the party's shadow education secretary, Bridget Phillipson, reiterated that to us this morning.

She said: "What we know from conversations with businesses is that there is a real appetite and willingness to invest here in the UK, and it's how government can work together with business to drive that investment to drive those jobs and the growth that will be so essential in the years to come.

"So it's not simply about the spending of public money, it's about how you lever in wider private investment."

She added that "everything in our manifesto is fully funded and fully costed".

Asked what happens if that private sector investment does not materialise, she dodged the question, reiterating that Labour has an "ironclad commitment" that they will "not increase VAT, income tax or national insurance".

We just spoke with Bridget Phillipson, Labour's shadow education secretary, and we asked about the junior doctors' strike and how quickly Labour would reach a settlement with them.

She did not give a timeframe, saying it's a "process of negotiation".

"But in order to get to a settlement, you've got to be around the table having that conversation.

"And that's something the Conservatives time and again have failed to do where it comes to our NHS."

She added that negotiations with junior doctors would be "a day one priority" if Labour is elected to government next week.

Asked if it is provocative for junior doctors to strike just days before the general election, she replied: "It's disappointing. I wish the strikes weren't going ahead.

"It represents a long-standing failure to reach an agreement."

As we reported, "money-saving expert" Martin Lewis hit out at the Conservative Party yesterday for tweeting a clip of him talking about a "private" conversation about an unnamed Labour policy that they linked to tax rises.

Asked if that is a sign of desperation, business minister Kevin Hollinkrake replied: "Well, I think if we've tweeted something that's inaccurate, that's wrong. We shouldn't do that.

"We don't need to be inaccurate because the Labour Party has been quite clear. I saw Wes Streeting on Laura Kuenssberg a few, a week or two ago, who said quite clearly the manifesto is not the sum total of its spending plans."

Junior doctors are today beginning a fresh round of strikes in their long-running dispute with the government over pay.

They are asking for a path towards restoration of pay to 2010 levels, which would be a rise of around 35%.

The shadow health secretary, Wes Streeting, has said if Labour wins the election on 4 July, he will meet junior doctors on 5 July to reach a solution, and we asked business minister Kevin Hollinrake if the Tories will do the same.

He replied that government "should always have a discussion, always sit across the negotiation table".

He said an offer to resolve the strike has "already been made", a 9% pay increase was given this year, and there were "discussions about a further 3% on top of that", which he described as "fair figures".

But on the broader demand for pay restoration, the minister said: "What the junior doctors are asking for is 35%, which taxpayers, of course, would have to fund.

"I don't think that's affordable. And it'd be interesting to see what Labour would do in the same situation."

Mr Hollinrake went on to say that the timing of this strike is "interesting".

"I'm not going to judge some of his motivations for decisions. But it is interesting timing, and I regret the decision to strike because we know this doesn't help waiting lists, which we want to bring down.

"We are bringing them down now, but part of the reasons why the waiting lists are as high as they are is partially because of the strike."

We've just been speaking to business minister Kevin Hollinrake, and we started with last night's fiery TV debate.

Sky's Wilfred Frost put to him that Rishi Sunak might have showed what could have been, if not for the various distractions and mistakes.

But Mr Hollinrake said the PM was "clear" that "this is a vote about the future, it's not about the past".

He talked about broader challenges over the last four-and-a-half years, such as COVID and the war in Ukraine.

But of Mr Sunak, he said: "You've got somebody with a clear plan, somebody who's proven in the heat of battle, has been able to execute well and put things in place that are important to people - or somebody with no plan and no answers."

He said he understands the "move for change in the country", but in an appeal to the country, added: "It'll be a disaster if Labour will get in. Don't settle for that. Don't settle for that kind of solution. Don't surrender to that."

Frost brought the minister back to the past five weeks and the betting scandal that continues to dominate headlines, and asked if politicians should be banned from betting on politics.

He replied: "I'm not against a ban on politicians betting on politics, but I think we should have a proper debate, proper look at this and make a decision that everyone's clear on what's expected and what's allowed and what's not allowed and to make a decision from there."

Separately, Mr Hollinrake admitted on Times Radio that he had placed a bet on the Tories to win the general election, but "not my seat, I think that would be wrong".

Sky News deputy political editor Sam Coates and Politico's Jack Blanchard are in your podcast feeds with their guide to the election day ahead.

This is day 36 of the campaign. Jack and Sam discuss closing arguments, the morning after the final debate, junior doctor strikes, their impact and the Trump v Biden factor.

👉 Tap here to follow Politics at Jack and Sam's wherever you get your podcasts 👈

By Faye Brown , political reporter

Jeremy Hunt has donated a further £32,000 of his own money to his constituency party as he tries to avoid becoming the first sitting chancellor in modern British history to lose his seat.

Figures from the Electoral Commission show in the first quarter of this year, Mr Hunt made three donations to the Godalming and Ash Conservatives.

The chancellor gave £7,794 across two separate donations on 2 January, then another £24,451.30 on 8 February.

The 8 February donation is the largest single donation he has made to the local association, which has received £166,457 from him in total since 2014.

The vast majority of that has been cash donations, official records show, though £8,918 of it was non-cash.

Under electoral rules, MPs and candidates can donate to their local constituency accounting unit if they wish.

The Lib Dems said Mr Hunt is "throwing the kitchen sink at keeping his seat", amid warnings he could be ousted come polling day on 4 July.

The Surrey constituency has been a key target seat for Sir Ed Davey's party as they aim to demolish the Conservative "Blue Wall" in southern England.

Read more here:

​​​​​​​The final TV clash of the election campaign was an ill-tempered shouting match, at least from Rishi Sunak. Sir Keir was more measured. More prime ministerial, perhaps?

As he had to as the underdog, Mr Sunak went on the attack from the start until the very end and unveiled a new campaign slogan: "Don't surrender…"

He said it no fewer than 15 times during the 75-minute debate. That's once every five minutes.

But just like the England-Slovenia Euros match 24 hours earlier, the result was a draw, 50%-50% exactly, according to pollsters YouGov.

And, some would say, that just like the England game, it was a 0-0 draw really.

Read Jon's full analysis here:

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