Lender Automation and Racial Disparities in Credit Access

• 成果类型: Article
• 署名作者: Howell, Sabrina T.; Kuchler, Theresa; Snitkof, David; Stroebel, Johannes; Wong, Jun
• 署名单位: National Bureau of Economic Research; University of Chicago; New York University
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13303
• 发表日期: 2024
• 页码: 1457-1512
• 关键词: motor-vehicle searches; DISCRIMINATION; BIAS; BUSINESSES; PREJUDICE; ETHNICITY; FinTech; wages; cost; RACE

摘要

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.