One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas

Revised: October 2024

WP 22-39/R – Improving fairness across policy domains often comes at a cost. However, as machine learning (ML) advances lead to more accurate predictive models in fields like lending, education, healthcare, and criminal justice, policymakers may find themselves better positioned to implement effective fairness measures.

A previous version of this working paper was originally published in November 2022.

Using credit bureau data and ML, we show that setting different lending thresholds for low and moderate income (LMI) neighborhoods relative to non-LMI neighborhoods can equalize the rate at which equally creditworthy borrowers receive credit. ML models alone better identify creditworthy individuals in all groups but remain more accurate for the majority group. A policy that equalizes access via separate thresholds imposes a cost on lenders, but this cost is outweighed by the substantial gains from ML. This approach aligns with the motivation behind existing laws such as the Community Reinvestment Act, which encourages lenders to meet the credit needs of underserved communities. Targeted Special Purpose Credit Programs could provide the opportunity to prototype and test these ideas in the field.

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Advancing Fairness in Lending Through Machine Learning

Showcasing the work of Philadelphia Fed researchers, this interactive data visualization explores an approach to credit lending using machine learning and fairness goals that may help address current disparities in credit access.