Over fifty countries have introduced regulatory sandboxes to foster financial innovation. This article conducts the first evaluation of their ability to improve fintechs’ access to capital and attendant real effects. Exploiting the staggered introduction of the UK sandbox, we establish that firms entering the sandbox see an increase of 15% in capital raised post-entry. Their probability of raising capital increases by 50%. Sandbox entry also has a significant positive effect on survival rates and patenting. Investigating the mechanism, we present evidence consistent with lower asymmetric information and regulatory costs

I ask whether machine learning (ML) algorithms improve the efficiency in lending without compromising on equity in a credit environment where soft information dominates. I obtain loan application-level data from an Indian bank. To overcome the problem of the selective labels, I exploit the incentive-driven within officer difference in leniency within a calendar month. I find that the ML algorithm can lend 60% more at loan officers’ delinquency rate or achieve a 33% lower delinquency rate at loan officers’ approval rate. The efficiency is maintained even when the algorithm is explicitly prevented from discriminating against disadvantaged social classes.

This article proposes an innovative method to assess borrowers’ creditworthiness in consumer credit markets by conducting machine-learning-based analyses on real-time video information that records borrowers’ behavior during the loan application process. We find that the extent of borrowers’ micro-facial expressions of happiness is negatively associated with loan delinquency likelihood, while the degree of fear expressions is positively associated with delinquency risk. These results are consistent with two economic channels relating to the adequacy and uncertainty of borrowers’ future income, drawn from the extant psychology and economics literature. Our study provides important practical implications for fintech lenders and policymakers

We investigate how bank organization shapes banking competition. We show that a bank's geographical lending reach and loan pricing strategy is determined by its own and its rivals’ organizational structure. We estimate the impact of organization on the geographical reach and loan pricing of a large bank. We find that the reach of the bank is smaller when rival banks are large and hierarchically organized, have superior communication technology, have a narrower span of organization, and are closer to a decision unit with lending authority. Rival banks’ size and the number of layers to a decision unit soften spatial pricing

Data-driven lending relies on the calibration of models using training periods. We find that this type of lending is not resilient in the presence of economic conditions that are materially different from those experienced during the training period. Using data from a small business fintech lending platform, we document that the small business credit supply collapsed during the COVID-19 crisis of March 2020 even though the demand for loans doubled relative to pre-pandemic levels. As the month progressed, most lenders significantly reduced or halted their lending activities, likely due to the heightened risk of model miscalibration under the new economic conditions

The 2020 CARES Act directed large cash payments to households. We analyze households’ spending responses using data from a Fintech nonprofit, exploring heterogeneity by income, recent income declines, and liquidity as well as linked survey responses about economic expectations. Households respond rapidly to payments, with spending increasing by about $0.14 per dollar during the first week and plateauing around 0.25–0.30 over 3 months. In contrast to previous stimulus programs, we see little response of durables spending. Households with lower incomes, greater income declines, and less liquidity display stronger responses whereas households that expect employment losses and benefit cuts display weaker responses

Financial markets have a central role in allocating resources in modern economies. One of the main functions of financial markets is the discovery of information. This information in turn helps guide decisions in the real side of the economy. The literature on the “feedback effect” of financial markets explores this channel. Empirical work tries to identify the informational feedback from markets to corporate decisions. Theoretical work explores implications that this feedback effect has for the equilibrium in financial markets and for economic efficiency. Current trends in information technology under the FinTech revolution change the nature of information processing in financial markets and so may change the nature of the feedback effect. In this article, I review the main themes of this developing literature and connect them to the current information revolution. I also discuss directions for future research

Bank deregulation and progress in information technology altered the geographical diffusion of banking structures and instruments, and reduced operational distance between banks and local economies. Although, the consolidation of the banking industry promoted the geographical concentration of banking decision-making centres and increased functional distance between local banking systems and local borrowers. This paper focuses on the impact that these spatial diffusion-concentration phenomena had on the financing constraints of Italian firms over the period 1996–2003. Our findings show that greater functional distance stiffened financing constraints, especially for small firms, while smaller operational distance did not always enhance credit availability