ESRB published a paper that presents evidence to indicate the direct role of information technology (IT) adoption in strengthening the resilience of commercial banks in US. The analysis finds that higher pre-crisis IT adoption led to 10% fewer non-performing loans during the global financial crisis. Loan-level analysis reveals that high-IT adoption banks originated mortgages with better performance and did not offload low-quality loans. Loans originated by high-IT banks experienced lower delinquency rates during the crisis, even when they were securitized and sold to Freddie Mac. Thus, the results indicate that IT adoption helped banks to select better borrowers and produce more resilient loans.
To understand the potential impact of higher technology intensity in lending on financial stability, the authors studied non-performing loans on the balance sheet of traditional U.S. banks with a heterogeneous degree of IT adoption during the global financial crisis and the subsequent consequences for credit provision. The evidence presented in the paper suggests that the “fintech era” is likely to be beneficial to financial stability. However, the main caveat of using the results of this analysis to inform the debate on fintech and financial stability is that the technologies adopted by commercial banks before the global financial crisis might be significantly different than the ones that banks, fintech, and financial arms of bigtech companies are implementing nowadays (including machine learning technologies). If one focuses on the lending business, there are several commonalities between the IT-intensive methods used before global financial crisis and the most recent advancements. Statistical models to predict defaults were widely used during the decade preceding the crisis. The up-to-date machine learning techniques that are used to predict borrowers’ behavior are more powerful versions of the previously available statistical tools, rather than radically different systems. The collection and use of new data to inform application decisions, such as the digital footprint is not conceptually different than the use of credit scores. The main difference lies in the requirements—in terms of infrastructure and know-how—to acquire, store, manage, and employ these data.
Related Link: Paper (PDF)
Keywords: Europe, EU, Banking, NPLs, Fintech, Bigtech, Regtech, Credit Origination, Credit Risk, Financial Stability, ESRB
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