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

Vitaly Meursault, Daniel Moulton, Larry Santucci and Nathan Schor
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Vitaly Meursault: https://www.philadelphiafed.org/our-people/meursault-vitaly

No 22-39, Working Papers from Federal Reserve Bank of Philadelphia

Abstract: Modeling advances create credit scores that predict default better overall, but raise concerns about their effect on protected groups. Focusing on low- and moderate-income (LMI) areas, we use an approach from the Fairness in Machine Learning literature — fairness constraints via group-specific prediction thresholds — and show that gaps in true positive rates (% of non-defaulters identified by the model as such) can be significantly reduced if separate thresholds can be chosen for non-LMI and LMI tracts. However, the reduction isn’t free as more defaulters are classified as good risks, potentially affecting both consumers’ welfare and lenders’ profits. The trade-offs become more favorable if the introduction of fairness constraints is paired with the introduction of more sophisticated models, suggesting a way forward. Overall, our results highlight the potential benefits of explicitly considering sensitive attributes in the design of loan approval policies and the potential benefits of output-based approaches to fairness in lending.

Keywords: Credit Scores; Group Disparities; Machine Learning; Fairness (search for similar items in EconPapers)
JEL-codes: C38 C53 G51 (search for similar items in EconPapers)
Pages: 54
Date: 2022-11-21
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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DOI: 10.21799/frbp.wp.2022.39

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