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Predictably unequal? The effect of machine learning on credit markets

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Title: Predictably unequal? The effect of machine learning on credit markets
Authors: Fuster, A
Goldsmith-Pinkham, P
Ramadorai, T
Walther, A
Item Type: Journal Article
Abstract: Innovations in statistical technology have sparked concerns about distributional impacts across categories such as race and gender. Theoretically, as statistical technology improves, distributional consequences depend on how changes in functional forms interact with cross-category distributions of observable characteristics. Using detailed administrative data on US mortgages, we embed the predictions of traditional logit and more sophisticated machine-learning default prediction models into a simple equilibrium credit model. Machine learning models slightly increase credit provision overall, but increase rate disparity between and within groups; effects mainly arise from flexibility to uncover structural relationships between default and observables, rather than from triangulation of excluded characteristics. We predict that Black and Hispanic borrowers are disproportionately less likely to gain from new technology.
Issue Date: 1-Feb-2022
Date of Acceptance: 17-Nov-2020
URI: http://hdl.handle.net/10044/1/85765
DOI: 10.1111/jofi.13090
ISSN: 0022-1082
Publisher: Wiley
Start Page: 5
End Page: 47
Journal / Book Title: The Journal of Finance
Volume: 77
Issue: 1
Copyright Statement: © 2021 the American Finance Association. This is the peer reviewed version of the following article, which has been published in final form at https://onlinelibrary.wiley.com/doi/10.1111/jofi.13090. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Keywords: Social Sciences
Business, Finance
Business & Economics
1502 Banking, Finance and Investment
Publication Status: Published
Online Publication Date: 2021-10-28
Appears in Collections:Imperial College Business School