Predictably Unequal? The Effects of Machine Learning on Credit Markets

Andreas Fuster, Paul Goldsmith‐pinkham, Tarun Ramadorai and Ansgar Walther
Authors registered in the RePEc Author Service: Paul Goldsmith-Pinkham

Journal of Finance, 2022, vol. 77, issue 1, 5-47

Abstract: Innovations in statistical technology in functions including credit‐screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)

Downloads: (external link)
https://doi.org/10.1111/jofi.13090

Related works:
Working Paper: Predictably Unequal? The Effects of Machine Learning on Credit Markets (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jfinan:v:77:y:2022:i:1:p:5-47

Ordering information: This journal article can be ordered from
http://www.afajof.org/membership/join.asp

Access Statistics for this article

More articles in Journal of Finance from American Finance Association Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().