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Eliminating Disparate Treatment in Modeling Default of Credit Card Clients

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  • Tom, Daniel M. Ph.D.

Abstract

A recent online search for model performance for benchmarking purposes reveals evidence of disparate treatment on a prohibitive basis in ML models appearing in the search result. Using our logistic regression with AI approach, we are able to build a superior credit model without any prohibitive and other demographic characteristics (gender, age, marital status, level of education) from the default of credit card clients dataset in the UCI Machine Learning Repository. We compare our AI flashlight beam search result to exhaustive search approach in the space of all possible models, and the AI search finds the highest separation/highest likelihood models efficiently after evaluating a small number of model candidates.

Suggested Citation

  • Tom, Daniel M. Ph.D., 2023. "Eliminating Disparate Treatment in Modeling Default of Credit Card Clients," OSF Preprints cfyzv, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:cfyzv
    DOI: 10.31219/osf.io/cfyzv
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    Cited by:

    1. Tom, Daniel M. Ph.D., 2023. "Special and General Segmentation-Separation Formulas," OSF Preprints mtucd, Center for Open Science.

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