We analyze the information content of a digital footprint—that is, information that users leave
online simply by accessing or registering on a Web site—for predicting consumer default …

In this article, we review some recent developments in the field of Financial Technology or
“FinTech.” We begin with an overview of what FinTech is and why it has become an …

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 …

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 …

We derive three testable predictions from a bank-P2P lender model of competition:(a) P2P
lending grows when some banks are faced with exogenously higher regulatory costs;(b) …

We investigate the effects of the collapses of Silicon Valley Bank, Signature Bank, and First
Republic Bank on the US financial sector by analysing returns and second moments of …

We exploit anonymized administrative data provided by a major fintech platform to
investigate whether using alternative data to assess borrowers' creditworthiness results in …

This paper compares the predictive power of credit scoring models based on machine
learning techniques with that of traditional loss and default models. Using proprietary …

This paper exploits strict flood insurance coverage limits and staggered flood map updates
to show that mortgage lenders offload flood risk to the government through flood insurance …

Abstract Information asymmetries are known in theory to lead to inefficiently low credit
provision, yet empirical estimates of the resulting welfare losses are scarce. This paper …