Advancing Fairness in Lending Through Machine Learning


Vitaly Meursault, Daniel Moulton, Larry Santucci, and Nathan Schor, with web adaptation by Kali Aloisi

April 2, 2024

Federal Reserve Research: Philadelphia

Advances in machine learning (ML) provide the opportunity to improve predictions that may expand credit access to more applicants. However, there is concern that gains from advanced models could accrue unequally between demographic groups or do little to reduce existing disparities in credit access. This research explores an approach using ML — paired with setting explicit fairness goals — that may help address current disparities in credit access and ensure that the gains from innovations in ML are more widely shared.

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