Eliminating Disparate Treatment in Modeling Default of Credit Card Clients

Daniel M. Ph.D. Tom

No cfyzv, OSF Preprints from Center for Open Science

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.

Date: 2023-01-17
New Economics Papers: this item is included in nep-big, nep-cmp, nep-pay and nep-rmg
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://osf.io/download/63c6b53264e50901335230e0/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:cfyzv

DOI: 10.31219/osf.io/cfyzv

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().