importance of information technology in banking essay

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IMF Working Papers

Does it help information technology in banking and entrepreneurship.

Author/Editor:

Toni Ahnert ; Sebastian Doerr ; Nicola Pierri ; Yannick Timmer

Publication Date:

August 6, 2021

Electronic Access:

Free Download . Use the free Adobe Acrobat Reader to view this PDF file

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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.

Working Paper No. 2021/214

Collateral Employment Financial institutions Job creation Labor Self-employment Technology

9781513591803/1018-5941

WPIEA2021214

Please address any questions about this title to [email protected]

• 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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• Does IT help? Information Technology in Banking and Entrepreneurship
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Financial technology and the future of banking

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The theory of the banking firm: a revision

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

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

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

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

Therefor it can be said that,

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

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

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

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

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

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

Theoretically, it can be shown that,

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

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

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

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

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

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

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

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

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

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

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

The evolution of the business model in a digital world

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

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

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

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

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

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

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

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

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

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

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

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

Open banking

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

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

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

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

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

Discussion: strategic options

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

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

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

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

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

The client retention strategy (incumbents)

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

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

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

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

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

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

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

The client acquisition strategy (challengers)

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

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

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

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

The banking as a service strategy (new entrants)

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

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

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

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

The social media payment strategy (disintermediators and disruptors)

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

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

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

Further research

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

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

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

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

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

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

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

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

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

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

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

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

Availability of data and materials

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

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Unlocking the full potential of digital transformation in banking: a bibliometric review and emerging trend

• Lambert Kofi Osei   ORCID: orcid.org/0000-0001-7461-4839 1 ,
• Yuliya Cherkasova 2 &
• Kofi Mintah Oware 1  

Future Business Journal volume  9 , Article number:  30 ( 2023 ) Cite this article

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Metrics details

Every aspect of life has been affected by digitization, and the use of digital technologies to deliver banking services has increased significantly. The purpose of this study was to give a thorough review and pinpoint the intellectual framework of the field of research of the digital banking transformation (DBT).

Methodology

This study employed bibliometric and network analysis to map a network in a single study, and a total of 268 publications published between 1989 and 2022 were used.

Our findings demonstrate that the UK, USA, Germany, and China are the countries that have conducted most of the studies on the digital banking transformation. Only China and India are considered emerging economies; everyone else is looking at it from a developed economy perspective. Additional research reveals that papers rated with A* and A grades frequently publish studies on digital banking transformation. Once more, the analysis identifies key theoretical underpinnings, new trends and research directions. The current research trend points toward FinTech, block chain, mobile financial services apps, artificial intelligence, mobile banking service platforms and sustainable business models. The importance of emphasizing the need for additional research in these fields of study cannot be stressed, given the expanding popularity of blockchain technology and digital currency in the literature.

Originality

It appears that this is the first study that examines the theoretical studies of digital banking transformation using bibliometric analysis. The second element of originality is about the multiple dimensions of the impact of technology in the banking sector, which includes customer, company, bank, regulation authority and society.

Introduction

The advent of information communication technology (ICT) is believed to have caused a paradigm shift in all aspects of human life. Technology has therefore become a necessary, unavoidable demand for society and the business environment, from work automation to service digitalization, from cloud computing to data analytics, from virtual collaboration to smart homes. Almost every industry is undergoing constant transformation because to technology. In the past 20 years, digitalization has had an impact on a variety of sectors, presenting fresh business prospects and encouraging new systems of innovation [ 1 ].

The finance sector is actively experimenting and inventing with the power of technology's digitization. It is also one of the industries that have successfully embraced digitization. One of the most laudable digital developments of the finance sector is the widespread adoption of digital banking over traditional banking methods. Recently, potentially disruptive technological breakthroughs and Internet-based solutions appear to have been introduced to the banking industry, one of the most established and conservative sectors of the economy. Digital transformation in banking is essential to enhance how banks and other financial organizations learn about, communicate with and satisfy the needs of customers. An effective digital transformation starts with understanding digital client behavior, preferences, choices, likes, dislikes, and stated and unstated expectations, to be more precise. Many academics are interested in how information and communications technology is advancing and how it can affect the banking industry [ 2 ]. However, the bibliometric analysis conducted by academics utilizing VOS viewer is assumed to be the first to look at the digital banking transformation (DBT) studies from a performance analysis and science mapping perspective.

Large data sets from databases like Web of Science, Scopus index or Dimension are permitted for bibliometric study. The bibliometric analysis moves the banks' digital transformation survey from single to multi-dimensional outcomes. A quick search of DBT studies shows that the first journal was published in 1989, despite the earliest forms of digital banking being traced back to the advent of ATMs and cards in the 1960s. The quantum of increase after 2014, amounting to 203 articles, representing 76% of all published articles on the topic, compels this study to focus on this field of DBT studies. We contend that establishing the area's intellectual framework is more crucial than ever. As a result, we make a contribution by offering a relevant, distinctive and significant intellectual map of the literature on digital banking studies through quantitative and bibliometric analysis. In mapping the intellectual structure of DBT, our study sets out to address the following critical research questions:

Who are the predominant contributors (publication by year, journals, publishers, authors, publication, journal quality, country, and universities) to the DBT theory?

What are the country's collaboration and citation analysis of the impact of digitalization on banks?

What is digital banking theory's intellectual foundation (co-citation)?

What are emerging research themes/trends and future direction (bibliography coupling

and keywords analysis) to digital banking theory?

In response to the above four questions, this study has at least four significant additions to the literature on digital banking. First, we extend and build upon prior assessments of digital banking by offering a factual, quantitative perspective on the theory's historical development across time. Of course, this study considers notable contributors, the intellectual framework and theoretical groundwork of the discipline, the degree to which individuals are connected, and thematic subdomains. We show how digital banking has advanced by evaluating the significant offshoots from the original work by [ 3 ]. Second, we objectively assess how faithfully emerging subtopic literature streams acknowledge and build upon Burk and Pfitzmann’s seminal works. As a result, our paper is uniquely suited to detect significant gaps that might exist in subtopic areas, and we offer suggestions for improving literature unification. Thirdly, we show how scholars of digital banking have historically changed their study goals over time in response to gaps between theory and practice in order to determine how faithfully they have addressed these gaps. Finally, we contribute to the digital banking literature by identifying emerging digital banking research and study trends. Overall, we think that our research exposes chances to grow more effectively and collaboratively in the future by highlighting well-traveled roads that previous researchers have taken, identifying potential cracks that may leave the literature in a state of disarray, and so forth [ 4 ].

This study used bibliometric and network analysis to map a network that comprises authors, co-authors, keyword occurrences, journal citations and author names in a single study. The approach can give a thorough overview and pinpoint the field's intellectual hierarchy [ 5 ]. Furthermore, according to [ 6 ], bibliometric approaches are suitable for mapping the academic structure of a certain area because doing so enables researchers to recognize "'what,' 'where' and 'by whom' founded the field. We carry out a thorough bibliometric evaluation to meet the research objectives by carefully extracting the sample literature using the proper inclusion and exclusion criteria and selecting the search string. The first stage involved a descriptive analysis, while the second stage involved a comprehensive bibliometric analysis. Utilizing VOSviewer and Rstudio assistance, citation and co-citation analyses were carried out to determine the intellectual structure of the study on digital banking studies. Weighted citation measures were used to identify the lead publications from the clusters.

The format of our paper is as follows: A brief theoretical overview of the DBT literature, including its core principles, significant developments and limits, is given in section " Theoretical background ." Section " Methods " describes the research approach in depth, and section " Results " shows the results of our investigation. The limitations of our study and their consequences for theory and practice are discussed in section " Discussions and future research agenda ." Finally, we provide our final observations in section " Conclusion ."

Theoretical background

Society, economics, banks and banking are changing as a result of technological advancement. Banks are an unneeded remnant whose purpose is best provided by alternate arrangements, even though we still need banking. The value chain of traditional banking has been disintermediated by technology, and its business model has been severely altered. As a result, Fin-Tech adoption and digital technology collaboration are widespread, constant and profoundly changing company structures [ 7 ]. Nearly 90% of banks fear losing business to Fin-Tech, which has replaced traditional value chains with shorter multi-modal and multi-directional nodes, according to KPMG's 2017 annual reports. Digitalization permeates the contemporary world, and the banking industry is no different. Our lives seemed to have grown so ingrained with digital technology that we would feel empty without it. Banks of all sizes are investing a lot in digital initiatives to maintain their uniqueness and meet as many of their customers' needs as possible. Digitalization leads to more customization and closer to customers. It is called digital banking when a bank renders its services online, and customers can make transactions and other activities online. Since over 73% of consumers use products from numerous platforms, Lee and Shin [ 8 ] highlight that bank model disruption and ascribe this to ongoing innovation followed by disruptive challenges, with the possibility of losing market share to Fin-Techs omnipresent.Mobile technologies and social media digitize bank value chains simultaneously addressing and influencing client demands and expectations.

However, according to our knowledge, not much research has been done on the banking sector. Nevertheless, it is well known that the banking sector, which is frequently IT-intensive, requires special consideration due to its significance for the whole economy. Berger [ 2 ] emphasizes that the benefits of technology adoption may not convert into improved production, which is consistent with the literature mentioned above. According to Berger, rather than the organization itself, the advantages of technology might be passed on to consumers and other production-related elements. Sharing data allow banks to process information more efficiently while also achieving huge economies of scale in the processing of payments. For instance, banks have reportedly employed information processing to handle deposit and loan client information as well as to more accurately assess risks, according to Berger and Mester. Additionally, they have employed telecommunications technologies to expeditiously process payments and disseminate this data while consuming fewer resources (2003, p. 58). This would imply that cost productivity increased in the 1990s.

Digital transformation has an impact on business processes and alters how banks conduct operations. A contributing aspect to the traditional relationship between customers and banks is digital transformation. Customers in particular have the right to use a variety of communication channels to engage in active and convenient engagement with banks and other customers via online customer support services. Most importantly, digital transformation enables banks to service a variety of consumers simultaneously, enhancing the bank's operational efficiency. In addition, the employee's job procedures are digitalized, reducing time and resources for both human resources and transaction execution. Thus, the bank will benefit from digital transformation by increasing output (raising the number of clients) and decreasing input expenses (reducing the number of employees and the time to make transactions).

The banking and FinTech industries will expand further in joint ventures, mergers and acquisitions toward convergence among banks, FinTech and technology organizations, and social media network providers as the new decade gets underway [ 9 ]. Digital technologies including blockchain, artificial intelligence (AI), data platforms, cybersecurity regulation technology and strategic collaborations will be well positioned to be retained in the banking business in a completely digitally changed financial environment [ 10 ]. Up until the advent of digital banking and the branch-based banking model in the early 1990s, traditional banking remained unaltered and unopposed. In the USA, Stanford Federal Credit Union opened the first online bank in 1994. The number of local bank branches has substantially decreased globally with the advent of online banking. Globally, the number of digital banks has been steadily rising at the same time. The first digital disruptor was ING Direct, which launched as an entirely online bank in 1996 and over the course of a little more than a decade attracted more than 20 million customers in nine countries without having to make any investments in physical infrastructure. In 2013, the FinTech bank "N26" received initial approval for a banking license. Amazon introduced an e-commerce-based checking account feature in 2021, while Facebook developed a social network-based banking service in 2020. By 2020, banking clients have been accustomed to using mobile banking apps, direct deposit to P2P payments and cloud-based banking platforms with AI.

To address our research issues in the present study, we employed two bibliometric analytic techniques. Since bibliometric analysis is quantitative, systematic, transparent and repeatable, it is strongly recommended for mapping the intellectual architecture of a literature stream [ 11 ]. The specifics of our research methodology and key conclusions are shown in Fig.  1 .

Flow chart of searching strategy and data collection process

To achieve its goals, this study suggests using publications and citations to analyze the performance of authors, institutions, countries and journals. Another unique approach used in this study is known as scientific mapping. Co-authorship analysis, clustering, citation analysis and keywords analysis are the approach factors [ 5 ]. Bibliometric approaches have been applied in recent investigations [ 12 , 13 ]. Then, we employ it to start the process of developing a bibliometric investigation [ 5 ]. The following actions are a part of the four-step process: data gathering and analysis, selecting the limiting criteria, data analysis, discussions and conclusions.

Defining the search terms

We started by conducting a methodical keyword search of the current literature on digital banking [ 14 ]. We extracted data from the Scopus index database. According to [ 15 ], Scopus has a larger journal than any other service that conducts data mining. As a result, this study made use of this database to mine data for its bibliometric analysis. To identify digital banking impact articles, we used the keyword methodology outlined by scholars who have recently conducted reviews of DBT. By concentrating primarily on work that has undergone thorough peer review, we aimed to maintain the academic integrity of our sample. Conference transcripts and book chapters were taken out of the analysis. Additionally, we excluded any non-English-language publications; 298 articles make up our final sample, which is deemed adequate for bibliometric study. These articles were published between 1989 and 2022. The keys words are: digital, bank, banking, business model, company, finance, economics and social sciences.

Keyword protocol applied in Scopus for extracting articles.

Data search and collection

As a result of several authors using the Scopus database for bibliometric analysis, it was chosen as the database from which the study's data were extracted [ 12 , 13 ]. In comparison with Web of Science and Dimension, the Scopus database has many indexed journals. The first stage of data extraction involved 295 publications with the titles "effect of digitalization on banks" and "digital transformation of banks" in June 2022. The following stage of the data processing was restricted to 268 English-language journals. The research is restricted to publications in the fields of banking, business management, accounting, economics, econometrics and finance. The last research search turned up 268 papers that were written between 1985 and 2022. Our literature review and bibliometric analysis are built on the foundation of the sample size of 268 articles. The method of data extraction is displayed in Table 1 .

This study raises different research questions covering contributors to DBT or impacts of digitalization on banks and banking, average journals and journal quality citation, digital banking intellectual foundations (co-citation), emerging research themes/trends and future direction (bibliography coupling and keywords analysis) in institutional theory.

Who are the predominant contributors to digital banking theory

This study responds to the first research question by addressing the dominant contributors to the DBT theory by using the following criteria: publication by year, journals, publishers, authors, publication, journal quality, country, and universities.

Publication by year

Figure  2 illustrates the number of DBT publications between 1989 and early 2022, recording 268 scientific publications. DBT received little attention from the scientific community in the early years from 1989 to 2005, recording as little as seven publications. The available data further show that publication increased slightly to sixty-seven (67) over a twenty (20) year period from 2006 to 2016. However, there was a dramatic change in this trend afterwards. Approximately 72 percent of these scientific publications, representing one hundred ninety-four (194) articles, occurred in the last six years. The figure further revealed that the years 2020 and 2021 alone accounted for 43 percent of all scientific publications in the field of DBT. Perhaps the havoc of Covid–19 and the strategic role of banks in successfully influencing the payment system architecture in particular resonated well with researchers to pay much attention to the field around this later period. While the quantity of publications has increased, publications within elite journals continue to grow. As recently as 2017, more over 40% of DBT research was published in prestigious publications. In fact, since 2017, the average annual proportion of publications in the top tier to all publications is 62 percent. As a result, our findings imply that the standard of published research has generally kept up with the volume of publications.

Trends in digital banking publication since 1989

Publication activity by country

Our findings also show that DBT research has a truly global reach, as shown by the participation of authors from 65 different countries. Figure  3 gives a graphic representation of the top countries publishing DBT research. For better clarity, the study limited Fig.  3 to cover countries with more than five publications. Although the publication of digital banking is international, it is interesting to notice that a significant portion of the work originates from a limited group of wealthy nations. More specifically, more than 46% of all published DBT studies come from the USA, UK, India, China, Germany, Netherlands, Hon Kong, Romania, Finland, Poland, Ukraine, Italy and Spain. Only China and India are from emerging economies. Figure  3 illustrates publication activities by country.

Top publishing countries on DBT

Publishing activity by journal

Two hundred thirteen different journals published the 268 articles in our sample. Table 1 lists the top publishing Journals. Based on publication count, we found that the leading journals for DBT include Financial Innovation, Journal of Cleaner Production, Journal of Economics and Business, International Journal of Information Management, Journal of Information Technology and Sustainability Journal. Our observation revealed that even though the Journal of Financial Innovation had only two publications, it claimed the top spot with two hundred and twelve citations total citation, given an average citation of one hundred and six. This study also used Australian Business School Council (ABDC) rating & ranking. Journal quality is rated and ranked by ABDC, with A* being the highest-quality journal, followed by A and B as the second- and third-best journals, respectively. According to the ABDC ranking, journal C is the lowest ranked. The data available to us have shown that the high-quality journals in class A and A* are publishing works on digital transformation. Three of the top five journals in our data are in the A class.

Publishing activity by author and organization

According to [ 16 ], bibliometric methodologies can be used to evaluate the intellectual influence of universities and their research personnel. To determine the sources of digital transformation in banking, we assessed the research output of individual academics and institutions. We found 598 distinct writers from 224 organizations publishing on the subject of banking digital transformation inside our dataset. The top publishing scholars and institutions are listed in Tables 2 and 3 . The descriptive statistics also show that [ 17 , 18 , 19 , 20 ] are the authors with the highest citation. In addition, the Financial University under the government of the Russian Federation, Comsats University—Islamabad, National Chiao Tung University—China and the State University of Management—Russia are the top four.

Country collaboration and citation analysis

Country collaborations of co-authors analysis.

The UK is the most productive nation in terms of publishing changes in digital banking. Australia, Canada, Indonesia and the Russian Federation have the lowest populations. Figure  4 demonstrates that, with seven linkages and 18 times as many co-authorships, the UK has the highest level of collaboration. Countries like China, Hong Kong and the Netherlands, each with six links, tie for second place. The inflow of overseas students completing second and third degrees in the UK and the US may be one reason there are more significant connections between the two countries [ 21 ]. Additionally, the UK and China are two other significant technology superpowers laying the groundwork for digitization. This might have inspired and drawn academics to carry out studies in the area.

Country collaboration of co-authors analysis

Citation analysis

The most read articles in the field of research on DBT were found through citation analysis. Citation analysis examines the connections between publications and finds the most significant publications in a given study area [ 5 ]. Similar studies that used citation analysis based on the Scopus database have also been looked at research [ 21 ]. The authors' and the study's primary focus are analyzed based on their citations in Table 4 . The Financial Innovation Journal and Journal of Cleaner Production publish the most-cited article. Liu et al. [ 22 ] and Yip et al. are the authors of these articles [ 23 ]. Even though publications on the evolution of digital banking began in 1989, the most highly cited papers are in 2016 and 2018, respectively.

Cluster analysis (results of reference co-citation analysis with reference map)

By conducting the co-citation analysis of references as previously described and grouping the references cited by papers on DBT into clusters, we next looked at the intellectual foundation and structure of the DBT to answer the third research question. The 268 papers in our sample used 8720 different references in total. Our examination of co-citations revealed five interconnected clusters with a total of 67 articles. At least 20 of the 268 papers in our sample, which contained all 67 of these reference articles, collectively cited them. In other words, these 67 publications are the quantitatively most significant references in the literature on the shift of banking into the digital age. Similarly, we used the weighted citation count provided by VOS viewer to ensure high-quality articles in cluster analysis. We looked at the top 5 articles in each cluster as presented in Table 5 , to find a common topic, and we labeled each theme accordingly, following [ 24 ]. We summarize the findings of the five most influential studies in each cluster. In the following sections, we give a quick overview of these reference clusters and how they integrate into the larger framework for digital banking (Fig. 5 ).

Co-citation network of the reference map

Cluster 1: Digital banking innovation

A cluster that established its boundaries improved its theoretical relevance and defined it as the first and most noticeable cluster to arise. Therefore, it makes sense that [ 25 ] are the most important tenet of this fundamental research stream. In 2022, digital transformation will continue to be a crucial trend in banking. The financial services sector is slowly changing as a result of technology, just like how it has affected other economic sectors. Physical bank branches have historically served as the primary point of contact for facilitating customer and retail banking transactions, according to [ 25 ]. Customers are continuing to transition from in-person to digital transactions as technology advances because of a complementary influence brought about by more access to digital banking services and an improved experience of new digital access, goods, services and functionality. They have developed a novel mapping technique for FinTech developments that assesses the extent of changes and transformations in four subfields of financial services: operations management, technological advancements, multiple innovations, and blockchain and other FinTech innovations. According to [ 26 ], the current wave of mergers and acquisitions in the financial services sector, combined with the broad availability of sophisticated technology, has increased competitiveness in the sector. Also, Henseler et al. [ 27 ] used discriminant validity assessment analysis to establish relationships between latent variables in business transformation. The digital banking revolution cannot go without challenges. All innovations encounter client resistance, claims [ 28 ] tested hypotheses using binary logit models comparing mobile banking adopters versus non-adopters, mobile banking postponers versus rejecters and Internet banking postponers versus rejecters using data from two comprehensive national surveys conducted in Finland ( n  = 1736 consumers). The value barrier is the main obstacle to the adoption of online and mobile banking, according to the study's findings. He also discovered that age and gender strongly influence decisions to adopt or reject. When [ 29 ] looked at the effect of cognitive age in explaining older people's resistance to mobile banking, they discovered that traditional and image barriers had an impact on usage, value and risk. All impediments, in turn, have an impact on resistance behavior. Furthermore, cognitive age was found to moderate these relationships. In order words, younger elders have limited or no resistance to DBT as opposed to elderly ones. All writers in this cluster agree that technology and evolving customer demands dramatically affect how banks operate in the twenty-first century. Indeed, the coronavirus outbreak has made it clear that banking institutions need to speed up their digital transitions. But the banking sector needs to modify its business models for front-facing and back-office operations to keep up with the changes and avoid potential upheavals. True digital banking and a complete transformation are built on implementing the most recent technology, such as blockchain cloud computing and Internet of Things (IoT).

Cluster 2: FinTech and RegTech in Banking

Scholars in this cluster preoccupied themselves with the concept of FinTech (Financial Technology) and RegTech (Regulatory Technology) thus the application of emerging technology to improve the way businesses manage regulatory compliance). They provided a range of viewpoints to make the disruptive potential of FinTech and its consequences for a more thorough financial ecosystem application in the banking and financial ecosystem easier to understand. Despite the widespread agreement that FinTech will have a big impact on the financial services industry, little academic literature has examined this topic, according to [ 30 ], citing [ 8 ]. Kindly assist with the changes.. Additionally, no accepted definition of FinTech has yet been established. On the other hand, according to Google, the query what is FinTech is presently ranked seventh among the most popular FinTech-related questions (Google, 2016b). He gave the most up-to-date definition of FinTech, which is a new financial business that uses technology to enhance financial activity. Contrarily, RegTech, or regulatory technology, uses cutting-edge tools and methods to assist financial institutions in enhancing their regulatory governance, reporting, compliance and risk management. According to [ 31 ] research, many desirable results might certainly be attained if regulators were willing to implement cultural change and integrate technical improvements with regulation. Such outcomes can include stabilizing the financial system, fostering systemic stability. The disruptive invention by [ 31 ] has the potential to improve consumer welfare, regulatory and supervisory outcomes, and the financial services industry's reputation. According to [ 10 ], the traditional business models of retail banks are seriously threatened by the emergence of digital innovators in the financial services industry. Lee and Shin [ 8 ] who contend that FinTech ushers in a new paradigm in which information technology drives innovation in the financial industry endorse this point of view. FinTech is hailed as a paradigm-shifting, disruptive innovation that has the power to upend established financial markets. The corporate world is quickly digitizing, shattering borders between industries, providing new opportunities and eliminating long-successful business models, according to [ 22 ], who added to the literature. They added that, on the plus side, growing digitalization presents opportunities, including the chance to take advantage of a solid customer connection and boost cross-selling. The dangers are typically precise and immediate, which is a drawback.

Cluster 3: The new digital business model of banks and other financial service providers

The papers in this cluster delved into the business model concept and, to a more significant extent, the new banking business model, which is technology-led. According to [ 32 ], business strategists and academics are paying more attention to business models as they try to understand how businesses create value and function well in order to gain a competitive advantage. Additionally, they argued that the digital economy had given businesses the chance to test out novel systems for networked value creation, where value is collaboratively produced by a firm and a big number of partners for a large number of users. The researchers came to the conclusion that four key themes are emerging, largely centered on the idea of the business model: as a new analytical unit, providing a systemic perspective on how to "do business," encompassing boundary-spanning activities (performed by a focal firm or others), and focusing on both value creation and value capture. These ideas are related and reinforce one another. Chesbrough [ 33 ] says that businesses must use their business models to commercialize novel concepts and technology. While businesses may make significant investments and have elaborate systems for investigating novel concepts and technologies, they frequently lack the ability to develop the business models that would be used to implement these inputs. He proposed that organizations should build the capacity to innovate their business models in order to make sound business decisions. Durkin et al. [ 34 ] did an excellent job investigating social media's role in a bank’s new digitally oriented business model. They suggested that social media had the power to profoundly alter customer-bank relationships and improve how the two sides communicate in the future. Their research shows that a wide range of clients regularly use transactional e-banking services. Loebbecke and Picot [ 35 ] presented a position paper that considers the factors driving how digitization and big data analytics drive the change of business and society. There is also discussion of the potential effects of digitalization and big data analytics on banking or employment, particularly in terms of cognitive work. Although several authors have recently proposed definitions of "business model," Shafer et al. [ 36 ] claim that none of them seem to be broadly recognized. This lack of agreement could be ascribed to the concept's interest from a variety of fields, all of which have connected it to something. To develop business models in the age of digital transformation, there must be an exponential shift in corporate culture and leadership concentration. The authors concur that banking is evolving as a result of a new wave of digital-only firms who are fragmenting the industry, componentizing products, and upending established business models. They claimed that switching from the previous business model to the new one is not the only way to succeed in this adaptable, fluid world. Instead, it will shift away from relying on a single, vertically integrated business model and toward a variety of non-linear models and value chain roles. In actuality, the Covid-19 epidemic has accelerated the development of business ecosystems for digital banking. Opportunities to develop, deliver and realize the value in new ways are made possible by digital technologies. The pipeline concept, the foundation of the classic universal bank, allows it to independently manufacture, sell and distribute products using its internal resources. This vertically integrated pipeline business model is disintegrating, making room for value chains that are becoming more fragmented and chances for new business models. A network of diverse business players from backgrounds including banking, insurance, pension, communications, real estate, education, healthcare service providers and IT are part of the new business model that the researchers have found. They work together to benefit each other through coexisting. The result of these developments and transformation is that financial services will continue to function in innovative and distinctive ways from those previously observed.

Cluster 4: Role of IT in banking

The fourth cluster concentrated on the crucial part information technology (IT) plays in the supply of financial services. According to [ 37 ], several banks have used information technology (IT) to provide consumers with a variety of more effective services. They think that in order to gain clients and boost profits in a cutthroat business environment, bank management must simultaneously use a variety of service channels. The majority of earlier research on IT investment in the banking sector has been on implementing cutting-edge IT-based service channels, including Internet banking, from the perspectives of clients [ 37 ]. From the standpoint of the bank, Barkhordari et al. [ 37 ] demonstrate that IT has a beneficial effect on performance by taking into account both the conventional physical and alternative IT-based service channels at once. They came to the conclusion that the purpose of using IT-related tools in banking is to forward a strategic, transformative objective. Due to the advancement of modern IT, the relationship between banks and their customers has changed substantially over the past few decades. They claimed that some of the examples include well-known innovations such as automated teller machines (ATMs), online banking (e-banking), and straight-through processing (STP), as well as others that have not (yet) gained widespread adoption, such as electronic cash (e-cash), or electronic bill presentment and payment (EBPP). At least the first has changed how people and businesses manage their finances and had an impact on the entire sector. They outlined how the aforementioned advances needed structures that took trends into account and might broaden the scope of current bank architectures to include horizontal and vertical integration dimensions. According to [ 38 ], enterprise architecture is typically represented by the following layers and design objects:

Product/services, market segments, corporate strategy goals, strategic plans/projects and interactions with customers and suppliers are all included in the strategic layer.

Organizational layer: Information flows, organizational units, roles/responsibilities, sales channels and business processes.

Applications, application domains, business services, IS functionalities, information objects, and interfaces make up the integration layer.

Software layer: programs, data structures, etc.

Hardware components, network components, and software platforms make up the IT infrastructure layer.

When it comes to transformations, architectures are really useful, because they integrate many layers. Creating new businesses or reorganizing old ones is transformation.

According to [ 32 ], organizations that are successful over the long term have basic principles and purposes that never change while continuously adapting their business strategies and operations to the external environment. IT's penetration of the banking industry falls under this category of business change. Liu et al. [ 22 ] contributed to the conversation by asserting that technological advancements like high-frequency trading systems (HFT) and algorithmic trading systems had altered the financial markets. The point is that information technology (IT) makes it possible to design complex products, improve market infrastructure, apply adequate risk management strategies and aid financial intermediaries in reaching geographically remote and diverse markets. The Internet has considerably impacted the delivery methods used by banks. The Internet has become an essential medium for distributing banking services and goods.

Cluster 5: Response to DBT

This fifth and final cluster considered the attitude of staff and clients toward DBT. If computer systems are not utilized, they cannot increase organizational performance. Unfortunately, managers' and professionals' opposition to end-user technology is a common issue. We need to comprehend why people accept or reject computers in order to better forecast, explain and promote user acceptance. The findings point to the potential for straightforward yet effective models of user acceptance factors, with practical utility for assessing systems and directing managerial actions aimed at addressing the issue of underutilized computer technology. Agarwal and Prasad [ 39 ] assert that a recent lack of user adoption of information technology breakthroughs is to blame for the frequently paradoxical link between investments in information technology and increases in productivity. They continued by saying that the academic and professional sectors had grown concerned about this paradoxical connection between spending on information technology and increases in productivity. The axiom that systems that are not used generate little value is an often proposed explanation for this relationship. Therefore, in order to achieve the expected productivity advantage, it is not enough to simply have the technology available; it must also be accepted and used effectively by its target user group [ 39 ]. The work of DeLone and McLean threw more light on technology acceptance. When [ 32 ] created a thorough taxonomy, they provided a more comprehensive picture of the concept of information system success. Six main characteristics or categories of the success of information systems are proposed by this taxonomy: system quality, information quality, utilization, user satisfaction, individual impact and organizational impact. Meanwhile, further discussions in this cluster have given more insights into customer acceptance or otherwise of IT in banking. Perceived utility, perceived ease of use, trust and perceived enjoyment are discovered to be immediate direct drivers of customers' views toward utilizing Internet banking, according to [ 40 , 41 ] research. This finding is consistent with some of the findings of other studies. The clients' behavioral intentions to utilize Internet banking are determined by attitude, perceived risk, fun, and confidence. Although the perceived website design has a direct impact only on perceived usability, its indirect effects on perceived usefulness, attitude and behavioral intentions are considerable. Perceived enjoyment only has a short-term impact on perceived ease of use, but both a direct and indirect influence on perceived usefulness. Customer experience is at the heart of the digital banking transition. Therefore, banks must continuously innovate products, integrate cutting-edge technology and add value for their clients.

Keywords analysis

The trends in the keywords displayed in multiple studies can be used to determine the main study direction for upcoming investigations [ 42 ]. The VOSviewer r software, which has previously been utilized by other writers, is employed in this study to extract the author's keywords [ 12 , 21 , 43 ]. A co-occurrences network is produced by the VOS viewer program as a dimensional map [ 12 ]. We used bibliographical author keyword analysis to examine our sample and determine whether there was any increasing or declining themes of interest per research question four. We discovered that writers of the 268 publications in our sample employed 829 keywords to indicate their scientific work, meeting the studies' threshold. Only 26 words, or around 3% of the total, were used at least four times. Our findings imply that the literature on DBT is incredibly heterogeneous. Indeed, according to the results of most recent articles, 80 percent of the authors' specified keywords were utilized precisely once. However, there are several keywords that authors frequently utilize to describe their works (Fig.  6 ). FinTech is the most often used keyword, with 25 occurrences and 29 links to other keywords, followed by digitalization, with 18 and 20 links. Reporting on Digital Transformation contains 13 instances and 18 links. The bibliometric map of author keywords is shown in Fig.  6 .

Bibliometric map of author keywords co-occurrence with five minimum occurrences and overlay visualization mode

The theme areas contemporary academics focus on can be seen by closely examining the map. The use of bibliographic coupling is based on the subject the authors are investigating. The digital transformation of financial service delivery was investigated by [ 43 ] from the perspective of Nigeria about chatbot adoption. A moderated mediated model was used by [ 44 ] to examine how blockchain technology was adopted in the financial sector during the fourth industrial revolution. Additionally, Karjaluoto et al. [ 19 ] looked at how users' perceptions of value influence their use of mobile financial services apps. Similarly, Podsakoff et al. [ 16 ] focused on enhancing the value co-creation process: artificial intelligence and mobile banking service platforms. Taking the discussion to a different dimension, Teng and Khong [ 45 ] worked on Examining actual consumer usage of E-wallets: A case study of big data analytics. David-West et al. [ 46 ] examined sustainable business models to create mobile financial services in Nigeria. Yip and Bocken [ 23 ] deepened the discussion and, in turn, looked at Sustainable business model archetypes for the banking industry. Finally, Niemand et al. [ 20 ] highlighted digitalization in the financial sector: a backup plan with a strategic focus on digitalization and an entrepreneurial attitude. Future research on financial services provided via e-wallets and mobile banking is the main emphasis of the second cluster. Authors are still studying entrepreneurship and digitalization in the supply of financial services. Future research is required in these areas of study because blockchain technology and digital currency are also gaining traction in the literature. The most popular search terms and the number of times they were used are displayed in Table 6 .

Discussions and future research agenda

The first paper on DBT was published by [ 3 ], and since then, both its audience and popularity have grown. Yet, the rapid rise in total publications across a wide range of specialist areas, notably during the last five years, has made it increasingly difficult for academics to ascertain the intellectual structure of the field. Existing qualitative assessments, which usually only address a small fraction of Digital Transformation in Banking while failing to accurately capture the entire body of work, have in some ways made the problem of theoretical specificity worse. It is rather tricky for a qualitative evaluation to describe more than 260 works over three decades. Thus, our research fills a critical vacuum in the literature by thoroughly (and quantitatively) mapping the digital banking domain, documenting its conceptual structure and suggesting its most likely future orientations. The theoretical underpinnings from which they have been developed, the subtopics and subthemes they have written about, and the notable historical contributors to DBT study (such as scholars, schools, and journals) are all identified in our work over time. Overall, our findings imply a considerable worldwide impact of digitization on banking, making it a truly global study paradigm. Additionally, the high number of citations for recent works shows that there is a great need for more research utilizing the DBT theoretical framework, suggesting that the field of study will continue to advance for a very long period. The study's structure is based on a wide range of goals and inquiries.

The initial research question aimed to characterize the increase in publication (document by year and county) and productivity of journals in terms of citations, top authors and institutions of studies on DBT. According to the data that are currently available, 174 papers, or 72% of all scientific publications, were published in the last six years, from 2016 to 2022. Also, prestigious journals carried out more than 40% of the publications. Therefore, our data imply that the quantity and quality of published research have typically stayed up. Our data also show that the research on the DBT is genuinely global in scope, as seen by the contributions of authors from 65 different countries. China and the UK are split equally, with India coming in second. It is essential to add that the BRIC (Brazil, Russia, India and China) countries perform well with publications. African countries like Ghana and Nigeria are equally showing promising signs of publications in this light. Regarding journal productivity, the study has revealed that articles on the banking industry's digital transformation are published in high-caliber journals in the A and A* classes. In our statistics, three top-five journals fall into the A category. These are the International Journal of Information Management (A*), Journal of Information Technology (A*), and Journal of Cleaner Production (A). We found 598 distinct writers from 224 organizations publishing on the subject of DBT inside our dataset. The descriptive statistics also reveal that Ranti et al. (2020) have the most citations, while the Financial University of the Government of the Russian Federation is the most productive institution in terms of the DBT, with seven publications.

The second research topic analyzes the co-authorship analysis and citation analysis by nation of authorship. Figure  3 shows that the UK has the maximum amount of collaboration, with 16 links and 18 co-authorships. China, Hong Kong and the Netherlands tie for second place with six linkages each. The increase in foreign students seeking second and third degrees in the UK and China may be one factor fostering closer ties between the two countries [ 21 ]. The UK and China are two other critical technological superpowers establishing the foundation for digitization. This might have attracted scholars and prompted them to conduct studies in the area. Future research might study the effects of digitization on banking on enforcing public and private sector regulations in emerging nations like Africa.

The third research question assesses the intellectual structure of the knowledge of DBT. This result was attained through citation analysis. Finding the most important publications in a specific field of study through citation analysis involves looking at the relationships between publications [ 5 ]. The primary point of contact for enabling retail banking and consumer transactions in the past has been actual bank branches. Customers are still transitioning from in-person to digital transactions as technology develops thanks to a complimentary effect brought on by increased access to digital banking services as well as an improved user experience of new digital access products, services and an improved user interface. Further research revealed that the banking sector's transition to digitization had increased competitiveness among service providers. The citation analysis highlighted the impact of FinTech on financial services innovations. According to [ 8 ], FinTech ushers in a new paradigm where information technology drives innovation in the financial sector. FinTech is hailed as a paradigm-shifting, disruptive innovation that has the power to upend established financial markets. We discovered that the corporate world is rapidly digitizing, removing industry barriers, opening up new opportunities, and dismantling long-established business structures. The concept of a business model and, to a greater extent, the new banking business model was also included in the analysis. The authors proposed that businesses build the capacity to innovate their business models since it makes good business sense. For instance, it has been seen that social media is significantly influencing the business models of some digitally focused banks. Social media, according to some, has the power to radically alter customer–bank interactions and improve how the two sides communicate in the future. If banks are to have an impact, they must transition from relying on a single, vertically integrated business model to multiple non-linear models and roles in the value chain. As a result of these developments and transformations, financial services will continue to operate in novel and unique ways from those previously observed. The study has proven beneficial for the use of IT in banking. IT-related tools are used in banking to advance a strategic transformational goal. The connection between banks and their customers has altered significantly over the past few decades with the development of contemporary IT. The most prevalent enterprise architecture layers and design items, according to [ 38 ], are the strategic, organizational, integration, software and IT infrastructure. It has been established that information technology (IT) enables the development of complicated products, enhances market infrastructure, implements efficient risk management techniques and enables financial intermediaries to access diverse and geographically dispersed markets. Despite the enormous advantages of digital banking, opinions on the systems are widely divided. Agarwal and Prasad [ 39 ] claim that a recent lack of user acceptance of information technology breakthroughs is to blame for the frequently paradoxical link between investments in information technology and productivity increases. They said that the counterintuitive connection between productivity increases and information technology investments had alarmed academic and professional groups. According to theories advanced by academics, digital technology, in general, and information systems, in particular, must fall under one of the following taxonomies to be accepted and used: system effectiveness, accuracy of the data, usability, user happiness, personal effect and organizational effect. The fourth research question looked at the future directions and emerging research themes and trends in studies of the digital banking transition. Future scholars are still interested in business models, FinTech, and DBT or banking. Additionally, the focus of the conversation is rapidly shifting to emerging and developing economies. Nevertheless, contemporary research areas include blockchain [ 44 ], mobile financial services apps [ 19 ], artificial intelligence and mobile banking service platforms [ 47 ], and sustainable business models [ 46 ]. The importance of highlighting the need for additional research in these fields of study cannot be overstated, given the growing popularity of blockchain technology and digital currency in literature.

Implications for theory

At least four substantial contributions to the body of DBT research, in our opinion, have been made by this study. We contribute primarily by expanding on current DBT reviews. While other reviewers have used qualitative methodologies, we may supplement and expand on such assessments by utilizing a thorough bibliometric study, allowing us to be more explicit about DBT's intellectual progress and structure. This is significant because it gives us a unique opportunity to highlight notable contributors and pinpoint the present and past origins of DBT research. Second, our quantitative analysis of bibliographic data demonstrates how DBT research has developed into its paradigm, which is supported by the original article by Bürk and Pfitzmann [ 3 ]. Third, we make a contribution by detecting rising and negative trends in subtopic areas, so identifying the subjects that are most likely to be studied in the future by academics. Fourth, by conducting a comprehensive assessment of DBT, we pinpoint areas where theory and practice diverge and evaluate the ways in which researchers have aided practitioners by modernizing DBT to comprehend and foresee the difficulties of "real-world" business.

Implications for practice

The banking sector, like other sectors, aspires to embrace contemporary practices and incorporate digital technologies into its operational procedures. This complicated collection of measures necessitates a methodical and considered approach, particularly in financial services where substantial sums of money and severe risks are at stake. DBT in this sense refers to several adjustments made to the banking sector to integrate different FinTech technologies to automate, optimize, and digitize procedures and improve data security. The processes and technologies employed in the financial industry will alter due to several small and significant changes implied by this process. The fundamental tendency of digital transformation, regardless of industry, is the integration of computer technologies, and Statista's analysis indicates that this trend will continue to expand. The challenges posed by introducing new digital innovations must be understood by stakeholders, who must also articulate solutions. Again, embracing digital technologies will involve taking on several tremendous risks; for this reason, bank executives must simultaneously establish and implement a strategy for managing those risks. If regulators utilizing technology to oversee and control the industry want to ensure solid financial stability in the economy, they must constantly be ahead of innovation risk with appropriate countermeasures. Digital banking involves the collection and processing of vast volumes of customer data. This raises the issue of data protection following regulations and international best practices. The DBT's third useful outcome is that it prompts organizational leaders to consider how their personal biases—which are the products of their histories, characteristics and experiences—might influence opinions and, ultimately, bank performance.

Limitations

We know that no study is faultless, and ours has its setbacks. While we made every effort to minimize problems, we nevertheless expect to offer insightful suggestions for future bibliometric and DBT studies. First, we used the Scopus database, a popular database used in bibliometric research, to gather our bibliometric data [ 48 ]. Even though Scopus contains the most data sources, it does not include all research databases on the transformation of digital banking. Furthermore, because this database has so many uses, using Scopus for data collection could likely lead to mistakes that show up when performing bibliometric analysis. To put it another way, errors might have happened if articles were mislabeled, and it is possible that the database completely missed publications important to our study [ 49 ]. To address this potential issue, we followed the best bibliometric analysis methods. For instance, we thoroughly purged duplicates and other forms of incorrect items from our data. Additionally, this research is restricted to English-language publications, and the subject only includes business, management, finance, economics, FinTech and banking digitalization. The data search will be enhanced, and the search restriction will be reduced using several databases.

This article assesses the intellectual landscape and future potential of the field of DBT research, as well as the influence of that research. The approach for this study is based on descriptive analysis, performance analysis and science mapping analysis, and it employs bibliometric analysis. The set was created based on 268 documents from the Scopus database that span the years 1989 to 2022. We demonstrate that DBT has continued to be a hot topic for academic research approximately three decades after its conception. Our findings also indicate that the UK, USA, Germany and China are the countries that have conducted most of the studies on the DBT. Only China and India are considered emerging economies; everyone else is looking at it from a developed economy perspective. We further categorize the body of research on DBT into five main clusters, including (1) Digital Banking Innovation, (2) FinTech and RegTech in Banking, (3) The New Digital Business Model of Banks and Other Financial Service Providers, (4) The role of IT in banking, (5) Response to DBT. Due to a significant influx of international students, the UK, China and Hong Kong continue to be the most collaborative countries. Additional research reveals that papers rated with A* and A grades frequently publish studies on DBT. Once more, the analysis identifies key theoretical underpinnings, new trends and research directions. FinTech, block chain mobile financial services apps, artificial intelligence, mobile banking service platforms and sustainable business models are currently researched. Given the rising popularity of block chain technology and digital money in the literature, highlighting the need for more research in these areas of study cannot be overstated. This study builds on previous reviews by objectively charting the inception and intellectual growth of the digital banking area and evaluating its future possibilities. In essence, this bibliometric study offers a distinct and original viewpoint on the evolution of DBT by carefully and objectively assessing prior material and concurrently offering a clear road map for future work.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author upon request.

Abbreviations

Digital banking transformation

Financial technology

Regulatory technology

Internet of things

Automatic teller machine

Artificial intelligence

Information technology

Information communication technology

Straight through processing

Electronic banking

Electronic cash

Electronic bill presentment and payment

High-frequency trading system

Electronic wallets

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Acknowledgements

The authors would like to graciously thank the Editor-in-Chief and the editorial team, and the two anonymous reviewers for their feedback in developing this paper. The writers also acknowledge Prof. Alfred Owusu, Dean of KsTU's Business School, for his guidance, inspiration and support. We appreciate his inventiveness and how it enabled us to clearly define the goal and possibilities of this effort. The authors also appreciate the helpful advice provided by Dr. Thomas Adomah Worae and Prof. Abdul-Aziz Iddrisu as we worked on the first versions of the manuscript. Finally, we would like to thank Riya Sureka, a research scholar at the Malaviya National Institute of Technology in Jaipur, India, for his advice on how to analyze bibliometric data using the ‘R’ and VOS viewer software.

This research received no external funding.

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All authors contributed significantly to the development of this article; LK generated the title, wrote the introduction, collection and analysis of the data, interpreted the co-citation analysis and put the manuscript together. YC reviewed the existing to conceptualize the study, reviewed the study and expanded the analysis. KM involved data generation from Scopus data base, software running, data analysis and review of the work. All authors read and approved the final manuscript.

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Lambert Kofi Osei holds a masters of business administration (finance option) degree from the Kwame Nkrumah University of Science and Technology. He is currently a PhD finance and banking student of Siberia Federal University, Russia. He is currently a lecturer at the Department of Banking Technology and Finance—Kumasi Technical University—in Ghana. He also holds an associated charted membership with the Chartered Institute of Securities and Investment—UK. Osei is certified expert in microfinance (CEMF) from the Frankfurt School of Finance—Germany. Osei has had considerable level of industry experience, with over 12 years managerial experience in the banking industry in Ghana including been the chief executive officer of Eman Capital. Prior to joining Kumasi Technical University, he was the National Chairman of Ghana Association of Microfinance Companies (GAMC)—an umbrella body of all microfinance companies in Ghana. Despite joining academia recently, Osei has made two publications of his work and a lot more articles are under completion stage to be sent for review. It is the goal of him to be an authority in the field of digital banking to impact businesses and societies.

Yuliya Cherkasova holds Ph.D. in economics and is a associate professor, School of Economics, Finance and Public Administration, Siberian Federal University. She is the chair of Digital Financial Technologies of Sberbank of Russia. Her research interests include banking prudential regulation of banks, digital economy and public finance. As a researcher, she has published more than 70 articles, 10 textbooks on topics, related finance and banking aria.

Kofi Mintah Oware has a Ph.D. in business administration (sustainability finance and management) from Mangalore University, India, and an MBA degree from Aberdeen Business School (Robert Gordon University—UK). He is currently a senior lecturer in the department of banking technology and finance. He is also a chartered accountant with membership from the Institute of Chartered Accountants (ICA), Ghana, and Institute of Cost Executive & Accountants (ICEA)—UK. Before joining academia, he worked in blue-chip companies for 12 years in various capacities, including chief accountant, head of finance and general manager for finance & administration in Ghana and research consultant to Aberdeen Businesswomen network in the UK. Among his key roles during industry experience include representing management in union negotiations and presenting the firm's financial reports in the corporate board meeting. In academia, he has 34 publications in various journal, including two "A" s under ABDC (Meditari Accountancy Research), three "B" s under ABDC (Social Responsibility Journal & Society and Business Review) and one C (South Asian Journal of Business Studies) all with Emerald publications. Also, he has 10 academic papers in various journals under review.

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Osei, L.K., Cherkasova, Y. & Oware, K.M. Unlocking the full potential of digital transformation in banking: a bibliometric review and emerging trend. Futur Bus J 9 , 30 (2023). https://doi.org/10.1186/s43093-023-00207-2

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DOI : https://doi.org/10.1186/s43093-023-00207-2

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

Role of Information Technology (IT) in the Banking Sector

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

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

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

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

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

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

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

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

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

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

Benefits of E-banking:

To the Customer:

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

To the Bank:

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

Impact of IT on the Service Quality:

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

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

Impact of IT on Banking System:

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

Traditional Banking Sector

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

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

The New Relationship Oriented Bank

Impact of IT on Privacy and Confidentiality of Data:

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

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

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

Related Posts:

• Market Risk Management in Indian Banks
• Online Banking (E-Banking) Strategies
• Customer Services in Commercial Banks
• Virtual Banking in India
• Universal Banking In India
• The Importance of Liquidity for Commercial Banks
• Indian Financial Network (INFINET) and It's Impact on Indian Banking System
• Introduction to Investment Banking
• Future of Indian Banking System
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5 thoughts on “ Role of Information Technology (IT) in the Banking Sector ”

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Does IT help? Information technology in banking and entrepreneurship

Information technology (IT) has dramatically changed how information is used in the financial sector. This may affect the supply of credit from banks, as a key function of banks is to screen and monitor borrowers. Lending to opaque borrowers, such as young firms and start-ups, is likely to be especially sensitive to such changes in IT. The reason is that young firms have not yet produced sufficient quantitative information, such as balance sheet data. Instead, lenders rely on soft information. As start-ups contribute disproportionately to job creation and productivity, but are often financially constrained, understanding how the IT revolution in banking has affected start-ups' access to finance is of paramount importance. Yet, direct evidence for the impact of lenders' IT capabilities on entrepreneurship is scarce.

Contribution

First, our paper relates to the literature investigating the effects of IT in the financial sector on credit provision and small businesses. Second, we speak to papers that analyse the importance of collateral for entrepreneurial activity. We provide first evidence that banks' IT adoption increases the importance of collateral in banks' financing of young firms. Third, we contribute to the recent literature that investigates how the rise of fintech affects credit-scoring and credit supply. An advantage of focusing on the variation in IT adoption among banks 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.

We build a model in which banks can screen firms either by acquiring information about firms and their projects or by requiring collateral. Crucially, IT makes it cheaper for banks to analyse hard information and thus rely on collateralised lending. This benefits start-ups, as they have not yet produced sufficient information and have to be screened through the use of collateral. The model thus predicts that IT in banking will spur entrepreneurship – and the more so when collateral value rises. Consistent with the model's implications, we find that job creation by start-ups is stronger in US counties with IT-intensive banks, especially during periods of rising collateral values.

This paper analyzes the importance of information technology (IT) in banking for entrepreneurship. To guide our 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 then empirical show that job creation by young firms is stronger in US counties that are more exposed to IT-intensive banks. Consistent with a strengthened collateral lending channel, entrepreneurship increases by more in IT-exposed counties when house prices rise. In line with the model's implications, higher startup activity does not diminish startup quality. Instrumental variable regressions at the bank level further show that IT makes banks' credit supply more responsive to changes in local house prices, and weakens the importance of geographical distance between borrowers and lenders. These results suggest that banks' IT adoption can increase dynamism by improving startups' access to finance.

JEL classification: G21, G14, E44, D82, D83.

Keywords: technology in banking, entrepreneurship, information technology, collateral, screening.

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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.

A brief history of IT in the banking industry

Technology plays a key role in the banking industry, maximising the efficiency of transactions and services. Over the decades, banking practices have changed significantly due to information technology. And the BNP Paribas group has often positioned itself at the forefront of such developments. Let us take a look back at a century of innovation, from the 1920s to the present day.

Read also Pierre Mounier-Kuhn, Mémoires vives – 50 ans d’informatique chez BNP Paribas , Paris, 2013

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Home » Education » Importance of Information Technology in Banking Essay

Importance of Information Technology in Banking Essay

A very important part of the essay writing process is having the facts and figures to support your argument. The statistics and data presented in an academic essay are not as relevant as they are to a Business Essay. The main purpose of any essay is to present information that helps to convince the reader that your points of view and facts are true and well supported. So how do you accomplish this task?

The role of Technology in the Bankruptcy Process has been a matter of constant discussion in Bankruptcy Courtrooms all over the country, and it seems there are some very important aspects that are often missed in these discussions. So lets look at one of the main aspects that is often overlooked.

What type of technology are you using in your business? Is it information technology or a different form of technology? If you are using computers in your business, you are probably using a form of technology known as “information technology” or sometimes referred to as “intelligent computer.” The use of this form of technology is important because it allows for rapid data collection and analysis.

What is the purpose of using any type of software in the Bankruptcy Process? If you are using information technology to collect and organize data, the main purpose would be to collect and sort of the data you are collecting. As this information is collected and organized into a usable format for future reference in court, you will need a system that allows the data to be properly organized for easy reference.

How does it allow for easy access to the data collected in a timely manner? This is because the data you are trying to gather will need to be organized and easy to retrieve. You should also find a system that makes the collection and sorting of this data easy for the personnel reviewing the data for a ruling.

Do you need any more information on this subject? Yes, I need to make another point here. When using the internet to access this data, you need to be sure the data is accurate, and up to date.

Is it possible to use a software to make this data up to date? Yes, there are companies that will pay for their own systems to make this information up to date for their clients and the courts.

Now you see why it is important to make sure the data is correct and up to date. By making sure the information is accurate, you are helping to keep the courts from dealing with errors. fraudulent cases where people are trying to escape their debts and get away with it.

Need I go on? It is time to start thinking about the software that you need to buy. If you are going to use any type of information technology in your banking essay, I want to make sure it is reliable, user friendly and easy to use by the personnel who are reviewing your data for a ruling.

The data you are gathering should be complete and accurate and able to be easily accessed by the judges and lawyers reviewing the data. You can purchase a piece of software that has the ability to make your data easily accessible.

Make sure you research the company that you are purchasing from before you make the purchase. to make sure they are reputable. Make sure they are very experienced in providing this type of software for you to use.

Using any type of software in your banking essay will help to make the process easy and allow you to quickly and efficiently gather and organize all of the data. The software can be customized for all types of businesses and any type of individual.

The Essay You Write

How to Write a Persuasive Essay About Technology in Schools