Lender Automation and Racial Disparities in Credit Access

A-Tier
Journal: Journal of Finance
Year: 2024
Volume: 79
Issue: 2
Pages: 1457-1512

Authors (5)

SABRINA T. HOWELL (not in RePEc) THERESA KUCHLER (not in RePEc) DAVID SNITKOF (not in RePEc) JOHANNES STROEBEL (National Bureau of Economic Re...) JUN WONG (not in RePEc)

Score contribution per author:

0.804 = (α=2.01 / 5 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

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.

Technical Details

RePEc Handle
repec:bla:jfinan:v:79:y:2024:i:2:p:1457-1512
Journal Field
Finance
Author Count
5
Added to Database
2026-01-29