Bank adoption and computer lending during the pandemic
The increasing adoption of digital innovations in the financial system is pushing the academic debate on its potential advantages or disadvantages to seek a solid base of empirical evidence. While previous studies have investigated the effects of information technology (IT) adoption on different banking outcomes (e.g. Beccalli 2007, Koetter and Noth 2013), results so far have not been conclusive and, with a few exceptions (Pierri and Timmer 2020, Kwan et al. 2021), have not yet been tested in times of crisis.
In our recent article (Branzoli et al. 2021), we exploit the Covid-19 pandemic – an unpredictable event that is likely to have reinforced the importance of digital prowess as a source of competitive advantage – to analyze the variations of credit between Italian banks associated with different ex ante levels of IT adoption. We find that IT-intensive banks increased their loans to non-financial corporations (NFCs) more than others in the months following the outbreak of the pandemic; the increase was economically significant even when nationwide mobility restrictions were lifted and public health conditions improved.
Measuring bank IT adoption
We measure the level of IT adoption by banks using unique IT cost data reported in the income statement and usage survey information digital technologies at the bank level. These are expenses incurred for the purchase of computer hardware (e.g. personal computers, servers, mainframes) or software, the remuneration of IT professionals (e.g. IT support engineers) and the outsourcing of IT services to external service providers. IT costs are normalized by the bank’s total operating costs. Figure 1, Panel A, shows the evolution of the IT cost / total cost ratio over time and across percentiles.
To assess whether a larger share of IT costs is related to a higher degree of IT adoption, we explore the relationship between banks IT spending and the use of digital technologies. We combine IT cost data with bank-level survey information on the state of the digital transformation of the Italian banking sector.1 Specifically, we ask banks to indicate the financial services they offer online (e.g. loans, payments, asset management), if applicable. Respondents are also asked if they have any innovative projects underway, what technology underlies them (e.g. big data, biometrics, artificial intelligence) and for what purpose (e.g. to improve consumer profiling or credit risk assessment). By controlling for a rich set of banking characteristics (including size, funding structure, and profitability), we find that our measure of IT adoption is in fact related to the degree of digitization and the propensity to innovate banks: the higher the IT spending, the more likely it is to offer digital services and engage in innovative processes.
Panel B in Figure 1 presents the dynamics of credit in Italy before and after the pandemic epidemic, depending on the degree of digitization of banks: since the start of 2020, credit driven by high technology banks (that is, those in the top quartile of the IT cost / total cost ratio distribution) grew 11%, double the rate recorded by other lenders.
Figure 1 Breakdown of IT costs (Panel A) and credit dynamics across the technological levels of banks (Panel B)
Remarks: The top graph shows the evolution of the 25th percentile, median, and 75th percentile of the IT cost / total cost ratio distribution for each year. The bottom graph shows the lending patterns between banks with varying degrees of IT adoption. All the banks in our sample are divided into three groups according to their IT costs / total costs ratio: low tech if they fall into the bottom quartile, medium technology if they are between the second and third quartile and, high technology if they are in the top quartile. The total amount of credit per bank is normalized to 100 based on the amount of outstanding credit in December 2019.
We also study the dynamics of credit and its allocation between NFCs. Using a difference-identification strategy, we find that the effect of IT on credit growth was larger for those borrowers hardest hit by the pandemic. NFCs located in areas of the country most affected by the pandemic2 saw a larger increase in loans from high-tech lenders. There is a positive variation in credit for companies operating in sectors deemed non-essential during containment and consequently forced to close their physical establishments. Small and medium-sized enterprises (SMEs) – more exposed than large firms to liquidity shortages – have benefited the most from the growth in lending fueled by technologically advanced banks.
Digital channels versus physical channels
Whether the technology reduces the effect of distance on lending decisions is the subject of debate (Petersen and Rajan 2002, Basten and Ongena 2020, Keil and Ongena 2020). In our analysis, we investigate the role of geographic proximity (between lenders and borrowers) in influencing the effect of technology adoption on credit during the pandemic. Figure 2 traces the physical and digital reach of Italian banks on the eve of the pandemic: at comparable technological levels, the dispersion of the distribution of branches reflects a strong heterogeneity in the economic models of banks. By exploring the relative importance of these two dimensions, we find that banks able to serve their customers through traditional and digital channels have shown the highest credit growth as of March 2020; in other words, we prove that physical locations are always important, when combined with a strong digital presence of the bank.
Figure 2 Distribution of physical channels versus digital channels
Remarks: The horizontal axis shows the ratio of IT costs. The vertical axis shows the percentage of provinces in which the bank has a branch. The size of the points corresponds to the total assets in millions of euros. All calculated in 2020.
We shine a light on the impact of adopting technology in lending during the Covid-19 pandemic. Our results suggest that banks with a higher degree of IT adoption before the pandemic gave more credit to NFCs as the crisis began to unfold. Better digital capabilities could have helped banks manage more loan applications than usual, improve workflow through automation, and streamline approval processes. We also show that, even under severe physical restrictions, customers still appreciated the opportunity to have face-to-face interactions with their bank. Our analysis paves the way for future research on the long-term consequences of digitization in the banking sector. As the trend of digital adoption is set to continue, banks must adapt to changing customer preferences and anticipate changes in competition. The implications for business model innovation are sure to come.
Note from the authors: The opinions expressed here are those of the authors and do not necessarily reflect those of the Bank of Italy.
Basten, C and S Ongena (2020), “Online mortgage platforms can enable small banks to improve their cross-regional diversification,” VoxEU.org, August 15.
Beccalli, E (2007), “Does IT investment improve banking performance? Testimony of Europe ”, Journal of banking and finance 31 (7): 2205-2230.
Branzoli, N, E Rainone and I Supino (2021), “The Role of Bank Technology Adoption in Credit Markets During the Pandemic,” Working Paper.
Keil, J and S Ongena (2020), “It’s the end of bank branches as we know them (and we feel good)”, Discussion paper.
Koetter, M and F Noth (2013), “Use of IT, Productivity and Market Power in the Banking Sector”, Financial Stability Journal 9 (4): 695-704.
Kwan, A, C Lin, V Pursianen and M Tai (2021), “Stress testing banks’ digital capacity: Evidence from the covid-19 pandemic”, Working paper.
Petersen, M and R Rajan (2002), “Does distance always matter? The information revolution in small business lending ”, Finance Review 57 (6): 2533-2570.
Pierri, N and Y Timmer (2020), “Tech in Fin before FinTech: The importance of technology in banking during a crisis,” VoxEU.org, August 9.
1 The Regional Bank Loan Survey (RBLS), conducted by the Bank of Italy on an annual basis, covers a large sample of Italian banks representing 90% of deposits of the entire banking system.
2 The severity of the pandemic is monitored using data on hospitalizations, deaths and changes in residential mobility at the province level.