Prestigious Brattle Group Prize for Prof. Andreas Fuster
© 2023 EPFL
Professor Fuster's winning paper, entitled "Predictably Unequal? The Effects of Machine Learning on Credit Markets" was chosen by the Editors and Associate Editors of The Journal of Finance from a pool of eligible papers published in The Journal during the prior year.
The prize, which is awarded annually for outstanding papers on corporate finance, was presented at the American Finance Association's annual meeting by the journal editors. The prize includes a cash award of $25,000.
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.