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Lender Automation and Racial Disparities in Credit Access

Journal: Journal of Finance

Date: 20240401

Author: Howell, Sabrina T.; Kuchler, Theresa; Snitkof, David; Stroebel, Johannes; Wong, Jun

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.

Link: Google Scholar

Key Findings

Fintech Lenders Serve More Black-Owned Businesses

Fintech lenders originated 53.6% of PPP loans to Black-owned businesses, while only accounting for 17.4% of all PPP loans

Automation Increases Lending Access

After banks automated their loan processing procedures, lending to Black-owned firms increased by 4.3 percentage points

Racial Disparities Higher in Areas with More Bias

The tendency of Black-owned firms to borrow from fintech lenders is stronger in areas with higher measured racial animus

PPP Lending by Institution Type

  • Fintech lenders had the highest share (26.5%) of loans to Black-owned businesses
  • Small banks had the lowest share (3.3%) of loans to Black-owned businesses
  • CDFIs and credit unions had moderate shares around 10%

Effect of Bank Automation on Black-Owned Business Lending

  • Pre-automation share of loans to Black-owned businesses was 4.4%
  • Post-automation share increased to 8.7%
  • Represents a 98% increase in lending to Black-owned businesses

Racial Disparity Across Bank Types by Bank Size

  • Large banks show higher automation levels and lower racial disparities
  • Small banks show lowest automation and highest racial disparities
  • Software spending correlates with reduced racial disparities in lending

Contribution and Implications

  • Automation in lending processes can help reduce racial disparities in credit access by removing human biases
  • Technology investments by financial institutions may help achieve more equitable lending outcomes
  • Findings suggest a need to examine automation potential in other financial services to reduce discrimination

Data Sources

  • Lender type chart constructed using data from Table I, Panel B showing share of PPP loans to Black-owned businesses by lender type
  • Automation effect chart based on results from Table V showing the impact of automation on lending to Black-owned businesses
  • Bank size and software spending chart derived from branch-level IT spending data discussed in Section II.A and Internet Appendix Table IA.VII