New Machine Learning Study Findings Recently Were Reported by Researchers at Federal Reserve Bank of Philadelphia (One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-income Areas): Machine Learning

2025 JAN 23 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Current study results on Machine Learning have been published. According to news reporting originating from Philadelphia, Pennsylvania, by NewsRx correspondents, 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.”

Financial support for this research came from Carnegie Mellon University, ETH Zurich.

Our news editors 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 editors, 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, 2024. 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)

The news editors 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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