The Fairness of Credit Scoring Models

Christophe Hurlin, Christophe P\'erignon and S\'ebastien Saurin

Papers from arXiv.org

Abstract: In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing a protected attribute (e.g. gender, age, racial origin) and the rest of the population. This can be unintentional and originate from the training dataset or from the model itself. We show how to formally test the algorithmic fairness of scoring models and how to identify the variables responsible for any lack of fairness. We then use these variables to optimize the fairness-performance trade-off. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, improved for the benefit of protected groups, while still maintaining a high level of forecasting accuracy.

Date: 2022-05, Revised 2024-02
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Citations: View citations in EconPapers (2)

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http://arxiv.org/pdf/2205.10200 Latest version (application/pdf)

Related works:
Working Paper: The Fairness of Credit Scoring Models (2021)
Working Paper: The Fairness of Credit Scoring Models (2021)
Working Paper: The Fairness of Credit Scoring Models (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2205.10200

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