Automation in Small Business Lending Can Reduce Racial Disparities: Evidence from the Paycheck Protection Program

Abstract

By enabling smaller loans, broader geographic reach, and less human bias in decision-making, process automation may reduce racial disparities in access to financial services. We find evidence for all three channels in a setting where private lenders faced no credit risk but decided who to serve: the Paycheck Protection Program (PPP), which provided loans to small businesses during COVID-19. Black-owned firms disproportionately obtained their PPP loans from fintech lenders, especially in areas with high racial animus. After traditional banks automate their loan application processes, their PPP lending to Black- owned businesses increases. Using application data to a marketplace lending platform, we show that Black applicants are less likely to get any PPP loan at all when conditionally randomly routed to conventional lenders, but equally likely to get a PPP loan when routed to fintech lenders, representing a real effect of the disparity across lender types. Our main findings also cannot be fully explained by racial differences in pre-existing banking relationships, contemporaneous firm performance, or fraud rates.

Keywords: Fintech, Automation, Small business loans, racial discrimination, PPP

JEL Classification: G21, G23, G28, G41, J15