Hundreds of equity market intelligence financial technology firms (FinTechs) have formed in the last decade. We assemble novel data to describe their capabilities, users, and consequences. Our data suggest that these FinTechs i) aggregate many data sources, including nontraditional ones (e.g., Twitter, blogs), and synthesize such data using artificial intelligence to make investment recommendations, and ii) change Internet users’ information discovery by serving as substitutes for traditional information providers. We evaluate some nontraditional data and find evidence suggesting that such data contain valuable information or “crowd wisdom” that links to informational efficiency. Overall, our findings are consistent with this innovation benefiting investors and markets

This article surveys research on the effects of digitalization on access to finance. We focus the review on access through fintech. We review the growth of three main fintech technologies, fintech lending (incl. peer-to-peer lending), crowdfunding and initial coin offerings. We discuss existing evidence on how fintech affects access to finance for firms and investors and consider the regulatory challenges it poses. We incorporate the papers in this special issue, underlining their significant contributions to our understanding of the digitalization of finance and its effects. Finally, we discuss the challenges of research in the digital finance area and propose some new avenues for future research

We study the personal credit market using unique individual-level data covering fintech and traditional lenders. We show that fintech lenders acquire market share by lending first to higher-risk borrowers and then to safer borrowers, and rely mainly on hard information to make credit decisions. Fintech borrowers are significantly more likely to default than neighbor individuals with the same characteristics borrowing from traditional financial institutions. Furthermore, they tend to experience a short-lived reduction in the cost of credit, because their indebtedness increases more than non-fintech borrowers after loan origination. However, fintech lenders’ pricing strategies are likely to take this into account

We analyze lending by traditional as well as fintech lenders during COVID-19. Comparing samples of fintech and bank loan records across the outbreak, we find that fintech companies are more likely to expand credit access to new and financially constrained borrowers after the start of the pandemic. However, this increased credit provision may not be sustainable; the delinquency rate of fintech loans triples after the outbreak, but there is no significant change in the delinquency of bank loans. Borrowers holding both loan types prioritize the payment of bank loans. These results shed light on the benefits provided by shadow banking in a crisis and hint at the potential fragility of such institutions when delinquency rates spike

We study the effects of technological change on financial intermediation, distinguishing between innovations in information (data collection and processing) and communication (relationships and distribution). Both follow historical trends towards an increased use of hard information and less in-person interaction, which are accelerating rapidly. We evaluate more recent innovations, such as the combination of data abundance and artificial intelligence, and the rise of digital platforms. We argue that the rise of new communication channels can lead to the vertical and horizontal disintegration of the traditional bank business model. Specialized providers of financial services can chip away activities that do not rely on access to balance sheets, while platforms can interject themselves between banks and customers. We discuss limitations to these challenges to the traditional bank business model, and the resulting policy implications

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 studies the impact of retail investors on stock liquidity during the COVID-19 pandemic lockdown in spring 2020. Retail trading exhibits a sharp increase, especially among stocks with high COVID-19–related media coverage. Retail trading attenuated the rise in illiquidity by roughly 40% but less so for high-media-attention stocks. Causality is addressed using the staggered implementation of the stay-at-home advisory across U.S. states. The results highlight that ample free time and access to financial markets facilitated by fintech innovations to trading platforms are significant determinants of retail-investor stock market participation

We document four secular trends about U.S. mortgage origination by traditional and FinTech lenders after the 2008-2009 financial crisis. First, since 2011, the overall number, size, and approval rate of small and medium-sized loans have been decreasing over time, relative to large loans. Second, the largest lenders redistribute their lending the most. Third, this loan-size redistribution of credit increases in the size of the lender. Fourth, the effects are stronger for mortgages further away from the conforming loan limit(s) in both directions. We argue that the supply of credit drives these secular trends, and we assess several potential economic mechanisms