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.

• Previous Article

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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The rise of digital banking: a paradigm shift in fintech.

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Founder and CEO of FortySeven Software Professionals , with over a decade of experience advising F500 companies and growth-stage startups.

We live in the era of digitalization. This is a well-known fact, and the banking industry specifically has recently been revolutionized by technology. The advent of digital banks—or neobanks as some refer to them—is changing the financial landscape. With the right approach, a digital bank can be more than just a platform for transactions. It can become a financial ecosystem, offering everything from banking to investment products all in one place.

This goes hand in hand with statistics that promise growth. A recent CB Insights report reveals that investment in fintech, although it saw a decline in Q1 of this year, it was the only sector to see an uptick in quarterly deal volume.

What Sets Apart Digital Banks?

Digital banks are essentially banks that operate without physical branches and are built to provide financial services remotely through digital platforms such as mobile apps and online portals. Of course, it reduces significant costs and therefore gives these banks the opportunity to invest in technologies to improve the business.

Digital banks utilize cloud computing to ensure scalability and reliability, enabling them to manage large volumes of transactions seamlessly. AI and ML can provide personalized banking experiences, fraud detection and predictive analytics. It's no surprise that the global AI in fintech market size was valued at $8.23 billion in 2021 and is projected to reach $61.30 billion by 2031.

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Blockchain offers an unprecedented level of security and transparency in transactions. Digital banks incorporate multiple cybersecurity measures, including end-to-end encryption, multifactor authentication, KYC and more. These measures ensure customers' financial information remains protected, making digital banking as safe as, if not safer than, traditional banking.

The Future Of Fintech Funding—A Closer Look

Despite the investment fluctuations, the future of fintech funding remains bright. The $7.3 billion invested in Q1 2024 across 904 deals, as reported by CB Insights , is no small feat. It's a sign of the capital's continued belief in the promise of fintech and digital banking. U.S.-based fintech companies led the charge for this quarter, securing $3.3 billion across 393 deals, followed by the European fintech sector with $2.2 billion from 203 deals. Asian fintechs ranked third, with $1 billion from 210 deals. Noteworthy transactions include a $430 million funding round for the U.K.'s Monzo, led by Alphabet in March, and significant raises by U.S. firms Bilt Rewards and Kore.ai in January, totaling $200 million and $150 million, respectively.

The Rise Of Digital Banks: A Response To Consumer Preferences

The rise of digital banks is not only a technological endeavor but also a response to shifting consumer preferences. Today's customers expect services that fit within their digital lives. They want to manage their finances the same way they shop, communicate and live: digitally, seamlessly and on-demand.

The success of early disruptors like Revolut only underscores this shift. Most digital banks offer accounts that are easier to open, have lower fees and often come with features that make them more customer-centric, like included insurance or the possibility to make investments easily. In my own practice, we’ve built more than 100 digital banks, payment institutions and neobanks for new players on the market, and the one common thread is the mindset of the entrepreneurs who run these businesses: They are all eager to make people's lives easier.

And we see this with digital banks providing customers with the ability to pay or invest on the go. It’s not only about the preferences of the customers but more about the necessity of the real world. As an entrepreneur and owner of a company that has been part of this fintech transformation since its inception, as we look ahead, our vision for the future of finance is one that is inclusive, innovative and, most importantly, digital.

Investing In Innovation: The Key To Digital Banking Success

The industry is highly competitive, with new players entering the market regularly and established banking institutions quickly pivoting to offer digital solutions. To stay ahead, these banks invest heavily in technology, user experience and security. There are a lot of ways to build your own digital bank, and one of them is the implementation of the concept of "banking as a platform," where digital banks open their APIs to third-party developers, allowing them to create complementary services. This approach fosters a rich ecosystem of financial tools and benefits both the banks and their customers.

Since the implementation of cutting-edge technologies is crucial for the success of digital banks, it underscores the necessity of selecting the right technology partners. It's vital to thoroughly vet potential partners to ensure they meet all technical and business requirements. This includes assessing the scalability of their solutions, transaction capabilities, data storage practices and the costs associated with each service.

Choosing a partner that aligns with your own vision for innovation and customer service is key. Attention must be paid to where information is stored to comply with data protection regulations and understand the financial commitment required for scaling infrastructure to meet growing customer demands. This is a mistake that many startups make. In order to avoid it, check first how scalable the solution is.

This comprehensive approach ensures that digital banks can provide secure, efficient and cost-effective services to their users, paving the way for sustained growth and success in the competitive financial landscape.

Final Thoughts

The digital revolution in banking is not just a trend; it's a significant metamorphosis that is shaping how we manage our money, conduct our business and plan our futures. Digital banks are the architects of a more accessible, personalized and efficient financial world. With the right vision and investment, they can direct the way for a financial future that is dynamic and adaptable.

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The Role of Technology in Investment Banking

Investment banking offers financial advice and aims to raise funds to finance specific global investment projects for clients. The workflow of an investment bank involves various processes and services. Therefore, technology becomes more crucial in this industry as it allows for improved access to high-quality financial services. Banks utilize artificial intelligence (AI), machine learning, and automation to address the challenges of the competitive environment. In particular, Bank of America presents the Global Technology Program that emphasizes the importance of technology in sustainable finance.

The role of technology in an investment bank is to reduce costs, evaluate opportunities with regard to investment, optimize processes, and manage risks. AI can be used to enhance working with reports and provide documents for data analysis. As can be seen, the Global Technology Program requires Business Analysts with relevant technical skills, which are vital for meeting the company’s objectives. Furthermore, the initiative at the investment bank highlights the importance of creative thinking. Bank of America aims to utilize technology to ensure efficient business activity, sustainable finance, and new growth opportunities. Innovative solutions can provide investment banks with increased revenue, rate of approval, and service provision, as well as better compliance with standards. AI and machine learning are beneficial for research and development at financial organizations.

Another aspect of technology that plays a pivotal role in investment banking is smart analytics, which helps the company explore customer needs based on their data. As a result, innovative services can be implemented to enhance the client experience. The Global Technology Program demonstrates the importance of innovation for efficient work. Overall, technology is essential for investment banking and associated services as it enhances processes and operations.

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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.

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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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Quick Glossary: Banking Technology

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Banks play a crucial role in both local and international economies by aiding the flow of money, managing deposits and withdrawals, lending money and facilitating transactions. The sector includes various types of banks, such as commercial banks, investment banks, central banks and cooperative banks.

As pervasive as banking is, there are still a plethora of concepts in the industry, making it difficult to have a good grasp of the sector.

This quick glossary, written by Franklin Okeke for TechRepublic Premium, will make it easier to learn and understand banking concepts.

Featured definition from the glossary:

Collateralized debt obligation

A complex financial product that pools different types of debt, such as loans, mortgages or bonds and breaks it down into various tranches or segments of risk and return. These tranches are then sold to investors as securities, often with different levels of risk and potential returns.

Boost your banking technology knowledge with our in-depth 25-page PDF glossary. This is available for download at just $9. Alternatively, enjoy complimentary access with a Premium annual subscription. Click here to find out more.

TIME SAVED: Crafting this content required 50 hours of dedicated writing, editing and research.

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Banking And It's Adoption Of Technology

A wide range of businesses are starting to rely heavily on artificial intelligence, and banking is no exception. AI has the potential to boost operating income by 20% and raise efficiency by up to 30%, according to research from US-based multinational corporation Accenture. In order to quantify the effects of generative AI on bank employees and the financial ramifications for a bank over a three-year period, according to a study utilizing publicly available employee data conducted by Accenture.

Blockchain technology has drawn a lot of attention in today's fast-paced digital scene, but the finance sector is unquestionably the most prominent. The technology is widely recognized for its primary attributes, which include accuracy, consensus, trust, transparency, and immutability. Thus, it makes sense that banks are deliberately switching from the conventional banking system to blockchain-driven models in order to improve security, usability, productivity, and creativity.

To explore the ties of technology and banking we recently had the opportunity to speak with Asheesh Pandey, Executive Director of the Bank of Maharashtra at their press conference. Pandey commented on the bank's stance on the adoption of blockchain saying, "We are working upon the blockchain adoption. Starting with CBDC, central bank digital currency or malumi. We will also be expanding towards that."

As for the role of AI in Banking he shared, "AI is the most talked about question right now. The three important things as far as the bank of Maharashtra is concerned. We are actually adopting the AI ML, already we have a matured system in place where the analytics is playing a very good role in Bangkok, now coming to AI we have onboarded three fintechs. Another is in the area of the call center customer service and the other one is in the particular area of credit underwriting."

However there is a wide spectrum of customers to cater to which includes the rapidly growing youth but also the loyal senior customers who can not be ignored and expected to adopt change without consideration. Pandey shared, "On one hand we have the next generation who do not want to visit the branch. So certainly being a public sector bank I need to cater that and I need to get that business in my PI."

Bank of Maharashtra (BoM) reported a 44.95 per cent year-on-year (Y-o-Y) jump in its net profit to Rs 1,218 crore for the January-March quarter (Q4) of 2023-24 on the back of a steady rise in net interest income (NII) and uptick in fees and commissions. Sequentially, the public sector lender's profit rose by 17.6 per cent from Rs 1,036 crore in the quarter ended December (Q3) of financial year (FY) 2024. Nidhu Saxena, the Managing Director and Chief Executive Officer, said the bank can comfortably support business in the ongoing financial year with current capital adequacy level.

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BBC presenter’s likeness used in advert after firm tricked by AI-generated voice

Exclusive: Liz Bonnin’s face used on insect repellant ads after faked message ostensibly granted permission

There was something strange about her voice, they thought. It was familiar but, after a while, it started to go all over the place.

Science presenter Liz Bonnin’s accent, as regular BBC viewers know, is Irish. But this voice message, ostensibly granting permission to use her likeness in an ad campaign, seemed to place her on the other side of the world.

The message, it turns out, was a fake – AI-generated to mimic Bonnin’s voice. Her management team got hold of it after they saw the presenter’s face on online ads for an insect repellant spray this week, something for which she did not sign up.

“At the very beginning it does sound like me but then I sound a bit Australian and then it’s definitely an English woman by the end. It’s all fragmented and there’s no cadence to it,” said Bonnin, best known for presenting Bang Goes the Theory and Our Changing Planet.

“It does feel like a violation and it’s not a pleasant thing,” she added. “Thank goodness it was just an insect repellant spray and that I wasn’t supposedly advertising something really horrid!”

Howard Carter, the chief executive of Incognito, the company behind the botched campaign, claims he was sent a number of voice messages by someone he thought was Bonnin. He said these voice messages “clinched it” for him that he was really speaking to her.

He had previously sought her endorsement before being approached by a Facebook profile adopting Bonnin’s identity. He claims the messages exchanged between the two led him to believe she was the real deal despite thinking the profile was “a bit suspect”.

The person assuming Bonnin’s identity gave Carter a phone number and email address. They also provided him with contact details from someone pretending to be from the Wildlife Trusts, the charity where Bonnin serves as president. He said the deal was negotiated via WhatsApp and emails. He also claims he spoke to one of the scammers impersonating Bonnin over the phone on at least one occasion.

On 13 March, Carter received a contract via email that he believed was electronically signed by Bonnin. The company sent £20,000 to an account linked to a digital bank on 15 March, bank statements show. Images of Bonnin for use in the campaign were sent five days later.

Further emails sent by Incognito went unanswered. The campaign launched on Monday, using quotes and images sent by the scammers. Hours later, Bonnin said on X that she did not agree to any endorsement with the company.

The person impersonating Bonnin texted Carter on Tuesday to apologise and said she was not handling her social media and also cited server and security issues. At this point, the penny dropped for Carter that he had been duped.

Bonnin said: “I’m very sorry for what the company has gone through. It’s not fun for them at all, but it’s a violation on both our parts. It is a reminder that, if it looks too good to be true and too easy, or a little bit strange, triple check or quadruple check.”

She added: “If somebody contacts you and says, ‘Hey, let’s not go through the professional route,’ then beware.”

Carter claims he did not get the deal signed off through Bonnin’s management agency because the person impersonating her said “she was doing us a favour, provided we do it direct with her and not involve her main agency”.

In a voice note heard by the Guardian, the AI-generated Bonnin says a number of stilted phrases, such as “I must thank you for providing clarity on the direction you envision for our collaboration”.

Two AI experts who assessed the voice note agreed it was likely to have been artificially generated. Surya Koppisetti, a senior applied scientist at the image detection startup Reality Defender said: “There are gaps and recitation speed issues that are consistent with AI-generated speech. The dialogue is inconsistent in accent.” Koppisetti added that the speech quality was “unusually clear despite the noise”.

Michael Keeling, a senior data scientist at the AI technology company Faculty , said the “steady, monotone” artificial background noise in the message “is a classic way of making something seem more realistic”. “If you’re listening to this from your phone on a busy street, it’s much easier to slip through that way,” he added.

Bonnin said the incident was a “warning message” about the potential pitfalls of AI. “There are many ways AI can be used to benefit society. We’ve heard it can be used to identify cancers , but it is also deeply unconcernedly not regulated enough. The technology is only going to get better and more sophisticated,” she added.

Incognito said it had reported the incident to the police and to its bank. In a statement, it said: “We hope [our CEO] falling for this elaborate scam will be a warning to others not to fall for the same or similar ruses. Sophisticated criminals using AI and other computer-generated communications have targeted Howard and Incognito a few times.

“Understandably, many companies do not want the public to hear they’ve been deceived because of the embarrassment and shame. As an ethical, transparently run company, we feel it is our duty to alert people and their businesses about the rise of clever deceptions like this. Howard and everybody else in our organisation apologises to Liz Bonnin and her associates for any harm we have inadvertently caused.”

The BBC did not respond to a request for comment. The Wildlife Trusts declined to comment.

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Why Metaphors Matter in AI Law and Policy

Will rasenberger.

Scholar warns that figures of speech play an outsized role in shaping artificial intelligence regulation.

Is artificial intelligence (AI) a “critical strategic asset” or a “generally enabling technology”? Is it “our creation” or an “agent”?

It depends on which aspects of the technology one cares about or wishes to regulate, Matthijs Maas , a Senior Research Fellow at the Institute for Law & AI, writes in a new article . He argues that although none of the above terms is strictly right or wrong, policymakers must assess and choose carefully the figures of speech they use in referring to AI. For better or worse, these figures of speech could determine the shape that AI policy takes, Maas notes.

Maas defines analogies and metaphors as “communicated framings” that analogize a thing or issue to something else, and in doing so, suggest how to respond to the thing or issue. Such framings are fundamental to human reasoning and policymaking, Maas avers .

Maas offers an “atlas” of 55 terms that policymakers and experts, as well as the larger public, use to refer to AI. Each invokes , either explicitly or implicitly, a reference to some other, generally better understood entity or process. Importantly, the terms reflect connotations of, and value judgments about, those other phenomena. As each of these framings highlight s certain qualities, benefits, or risks of AI, they obscure others.

As a result, the 55 terms Maas provides can be contradictory. For instance, conceptualizing AI as a “black box” implies a lack of transparency and inability to comprehend how AI tools function, whereas “organism” centers the “development, evolution, mechanism, and function” of AI tools as discoverable and explainable phenomena.

Whether policymakers will address a given AI-related issue may depend on how “regulatory narratives” frame the issue and whether the framing resonates with policymakers on an intuitive level, Maas suggests. In shaping government perceptions of a technology, metaphors and analogies affect how a government program develops its regulatory priorities.

For example, Maas observes that the U.S. military’s early use of the term “cyberspace” analogized the internet to another domain of conflict, alongside land, sea, air, and space. This strategic rhetoric helped justify the military’s involvement in cybersecurity and the creation of the U.S. Cyber Command under the Department of Defense, Maas contends .

A likely analogy regulators of AI could draw on is consumer “products,” according to Maas.  A regulatory program’s framing of AI systems as “products” may imply that the technology can be subsumed under existing frameworks for product safety and redressing consumer harms.

But this framing is not without risks.

Framing AI as “products” with attendant consumer harms could divert attention from risks present in the development of AI products, Maas argues. It could also obscure dangers AI tools may pose to humans’ fundamental rights, if such rights are not easily conceived as consumer harms.

Mass also predicts that metaphors will play a critical role in legislation and in the courts.

Maas highlights the “battle of analogies” in recent class action lawsuits against the companies Midjourney and Stable Diffusion, which make AI image generators. In these lawsuits, the parties disagree over whether the programs are mere “collage tools,” that copy and regurgitate human-made images, or “art inspectors,” that make careful and sophisticated analyses of the human works they train on. If one of the metaphors gains traction, it could affect how the courts rule on whether the AI companies have violated copyrights or made non-infringing “fair use” of human works, Maas suggests.

A perhaps more important issue, Maas notes, is how Section 230 of the Communications Decency Act (CDA) will be applied to AI-generated content. The resolution of the matter could turn on which existing technologies the U.S. Congress or federal courts analogize AI to.

Section 230, passed by the U.S. Congress in 1996, protects web platform operators from liability for carrying illegal content created entirely by third parties. But “information content providers”—those who create or contribute to unlawful content—can be held liable under 230.

If courts interpret AI chatbots such as ChatGPT as, or as similar to, a “search engine,” then the makers of such bots will likely be immune from liability, Maas explains. Given their ability to generate novel content, however, courts could also analogize chatbots to information content providers, which would subject AI companies to liability for their chatbots’ outputs under Section 230. The latter framing could seriously implicate the future viability of many AI products, Maas notes.

Regulators and courts probably have no option but to analogize AI to earlier technologies, Maas recognizes . Indeed, the already existing frames associated with these technologies could be quite helpful in regulating AI, says Maas. He warns, though, that the further removed an analogy is from the unique characteristics of a largely unprecedented technology, the more likely it is to have dangerous implications.

Maas urges policymakers and lawmakers to analyze the figures of speech related to AI they hear and consider what other metaphors might be more suitable and useful. Importantly, regulators should consider which features a given metaphor highlights and which it occludes, Maas highlights. Otherwise, the hasty adoption of metaphors could lead to inhibited understanding of AI and even ineffective and harmful laws, Maas warns ,

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Protecting Against Sexual Violence Linked to Deepfake Technology

Scholars and researchers navigate the evolving challenges posed by deepfake technology.

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Huawei’s consumer CEO Richard Yu shifts role

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Thursday, 02 May 2024

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Rapid advance: A Huawei Technologies Co store in Beijing, China. Last year, the company saw its fastest growth in four years, in part due to a rebound in the consumer segment Yu had headed. — Bloomberg

SHANGHAI: China’s Huawei Technologies has moved the high-profile chief of its consumer business, Yu Chengdong, to a new role as chair of the unit, two people with knowledge of the matter say.

While Yu becomes chairman, He Gang, the consumer business group’s chief operating officer, will take over as the unit’s chief executive officer (CEO), the people said. The change was announced internally last week when it took effect, one said.

Yu has headed Huawei’s consumer unit, which includes its smartphone business, since 2012, a period marked by rapid growth, a hit from US sanctions to the company’s supply chain and more recently a rebound in sales.

Yu’s role as chairman of Huawei’s smart car solutions business, a unit the company has said it intends to spin off into a new company, is unchanged, the person said.

Huawei did not comment and Yu, also known as Richard Yu, could not be reached for comment.

No reason was provided in Huawei’s internal announcement for the change, a second person with knowledge of the matter told Reuters.

The people asked not to be named because they were not authorised to speak on the matter.

As CEO, Yu would be expected to take charge of business strategy, and as chairman, he would handle administrative matters, including personnel, the person said.

Yu is one of Huawei’s highest-profile executives. He has spoken at major industry events and appeared with Chinese bloggers to pitch products and Huawei’s innovation.

In September, Yu appeared at a Huawei product launch where supporters packed a stadium and chanted “far, far ahead,” a phrase that has gone viral in China to describe Huawei’s competitiveness.

Yu was also central to the launch of a Huawei-backed electric vehicle, the Aito M7 SUV, which is made in partnership with Seres Group.

From 2019, the US government has restricted Huawei’s access to American technology, accusing the company of being a security risk, which Huawei denies.

But last year, Huawei saw its fastest growth in four years, in part due to a rebound in the consumer segment Yu had headed.

Meng Wangzhou, Huawei’s chief financial officer and the daughter of the company’s founder, said last year that Huawei was no longer in the crisis mode it had entered with US sanctions. — Reuters

Tags / Keywords: HuaweiReshuffle , YuChengdong , ConsumerBusiness , HeGang , HuaweiSmartphone , USSanctions , HuaweiEV

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