Predictably Unequal? The Effects of Machine Learning on Credit Markets

Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, Ansgar WaltherFinance金融中介UTD24

Journal of Finance2021-10-28DOI

Citations394

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.

Flexibility (engineering)Statistical learningStatistical discriminationEconometricsMachine learningArtificial intelligenceSimple (philosophy)Computer scienceRace (biology)EconomicsStatisticsMathematics