This article studies how the rise of financial technology (Fintech) lending affects credit access, interest rates, and social welfare. We consider a lending competition model with two incumbent banks and a Fintech lender, which use different information and technologies to assess borrower creditworthiness. We show that Fintech lending could negatively affect high-quality borrowers’ access to credit when the Fintech lender’s screening accuracy is superior to that of the banks. Furthermore, Fintech lending may worsen the allocative efficiency of credit and reduce social welfare under some conditions. Analytical and numerical results suggest that Fintech lending mostly reduces the expected interest rates

We describe the dimensions along which bank technologies differ from fintech competitors and construct a novel measure of a bank’s technology based upon its overlap with fintech firms in terms of granular product installation data. A one standard deviation increase in our financial technology measure is associated with an 8.3 percentage point increase in Paycheck Protection Program (PPP) loans in 2020Q2. We show that smaller banks benefited more from marginal technology gains, that technology facilitated out-of-area lending, and that technology complemented small banks’ branch-based in-area lending. In a difference-in-differences analysis, we show an outsized increase in small business lending growth in 2020 for high tech small banks relative to their peers

Small business lending (SBL) plays an important role in funding productive investment and fostering local economic growth. Recently, nonbank lenders have gained market share in the SBL market in the United States, especially relative to community banks. Among nonbanks, fintech lenders have become particularly active, leveraging alternative data and complex modeling for their own internal credit scoring. We use proprietary loan-level data from two fintech SBL platforms (Funding Circle and LendingClub) to explore the characteristics of loans originated pre-pandemic (20162019). Our results show that these fintech SBL platforms lent relatively more in zip codes with higher unemployment rates and higher business bankruptcy filings. Moreover, fintech platforms’ internal credit scores were able to predict future loan performance more accurately than traditional credit scores, particularly in areas with high unemployment. Using Y-14 M loan-level bank data, we compare fintech SBL with traditional bank business cards in terms of credit access and interest rates. Overall, while not all fintech firms follow the same approach, we find that fintech lenders could help close the credit gap, allowing small businesses that were less likely to receive credit through traditional lenders to access credit and potentially at lower cost

Using a sample of U.S. community banks and FinTech loans data from LendingClub and Prosper, I find that banks’ future change in risk-taking is positively associated with their current exposure to FinTech penetration. Path analysis shows that FinTech penetration influences bank risk-taking through the erosion of bank charter value. Additionally, cross-sectional analysis shows that the risk-increasing effect of FinTech penetration is stronger for banks with lower ex-ante charter value and greater reliance on hard information. My results are robust to alternative measures of bank risk-taking and FinTech penetration, propensity score matching, and a battery of sensitivity and additional tests. Regarding policy implications, the findings imply that reasonable estimations of banks’ charter value may serve as an early indicator of banks’ future risk-taking incentives

Minnis, Sutherland, and Vetter (MSV) documents a sharp decline in lenders’ collection of attested financial statements (including unqualified audits, reviews, and compilations) over the period 2002 to 2017. They attribute this change to lenders adopting new technology and new non-bank lenders entering the lending market. In this discussion, I explore several dimensions of their findings. First, I provide a framework for usefulness of financial statement information in debt contracting. Using this framework, I consider how financial statements may be useful for the small and medium loans the authors study, and how this role could be disrupted. Second, I consider how financial technology (FinTech) has disrupted traditional lending and potentially changed the role of financial statements. Finally, I consider the implications for this change on the accounting profession

We study the effectiveness of saving goals in increasing individuals' savings using data from a Fintech app. Using a difference‐in‐differences identification strategy that randomly assigns users into a group of beta testers who can set goals and a group of users who cannot, we find that setting goals increases individuals' savings rate. The increased savings within the app do not reduce savings outside the app. Moreover, goal setting helps those individuals previously identified as having the lowest propensity to save. Matching App user survey responses to their behavior highlights the relative merits of monitoring and concreteness channels in explaining our findings

Central banks around the world are exploring the possibility of Central Bank Digital Currencies (CBDCs) for retail and wholesale use. While no major economy is yet to fully introduced a CBDC, some countries have begun pilot programs. The purpose of this paper is to highlight the potential benefits and risks associated with CBDCs, including challenges and opportunities associated with proposed CBDC regulation in the United States and the European Union. The paper also discusses the CBDC landscape in Asia. It highlights some of the key findings of the research presented in this special issue on FinTech and CBDCs. Lastly, the paper offers thoughts for potential future research in areas such as the actual designs of CBDCs and their uses, ‘DeFi’ versus ‘CeFi’, their interoperability and stability, and concerns over cybercrime

Do coordination failures constrain financial technology adoption? Exploiting the Mexican government’s rollout of 1 million debit cards to poor households from 2009 to 2012, I examine responses on both sides of the market and find important spillovers and distributional impacts. On the supply side, small retail firms adopted point-of-sale terminals to accept card payments. On the demand side, this led to a 21 percent increase in other consumers’ card adoption. The supply-side technology adoption response had positive effects on both richer consumers and small retail firms: richer consumers shifted 13 percent of their supermarket consumption to small retailers, whose sales and profits increased

When race is not directly observed, regulators and analysts commonly predict it using algorithms based on last name and address. In small business lending—where regulators assess fair lending law compliance using the Bayesian Improved Surname Geocoding (BISG) algorithm—we document large prediction errors among Black Americans. The errors bias measured racial disparities in loan approval rates downward by 43%, with greater bias for traditional vs. fintech lenders. Regulation using self-identified race would increase lending to Black borrowers, but also shift lending toward affluent areas because errors correlate with socioeconomics. Overall, using race proxies in policymaking and research presents challenges

Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. We document these findings in the context of the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black‐owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black‐owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, preexisting banking relationships, firm performance, or fraud rates