Studies from Federal Reserve Bank of Philadelphia Reveal New Findings on Machine Learning (One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-income Areas): Machine Learning

2026 AUG 14 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Philadelphia, Pennsylvania, by NewsRx journalists, research stated, “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.”

The news reporters obtained a quote from the research from the Federal Reserve Bank of Philadelphia, “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.”

According to the news reporters, the research concluded: “Targeted Special Purpose Credit Programs could provide the opportunity to prototype and test these ideas in the field.”

This research has been peer-reviewed.

For more information on this research see: One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-income Areas. Journal of Policy Analysis and Management, 2025;44(3):792-815. Journal of Policy Analysis and Management can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Journal of Policy Analysis and Management - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1520-6688)

Our news correspondents report that additional information may be obtained by contacting Vitaly Meursault, Federal Reserve Bank of Philadelphia, 10 Independence Mall, Philadelphia, PA 19106, United States. Additional authors for this research include Daniel Moulton, Larry Santucci and Nathan Schor.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1002/pam.22662. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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