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The Fairness of Credit Scoring Models

Author

Listed:
  • Hurlin, Christophe

    (University of Orleans)

  • Pérignon, Christophe

    (HEC Paris)

  • Saurin, Sébastien

    (University of Orleans)

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.

Suggested Citation

  • Hurlin, Christophe & Pérignon, Christophe & Saurin, Sébastien, 2021. "The Fairness of Credit Scoring Models," HEC Research Papers Series 1411, HEC Paris.
    • Handle: RePEc:ebg:heccah:1411
    DOI: 10.2139/ssrn.3785882
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    References listed on IDEAS

    as
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    Cited by:

    1. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    2. Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," LawFin Working Paper Series 42, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    3. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    4. Langenbucher, Katja, 2022. "Consumer credit in the age of AI: Beyond anti-discrimination law," SAFE Working Paper Series 369, Leibniz Institute for Financial Research SAFE.

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    More about this item

    Keywords

    Fairness; Credit scoring models; Discrimination; Machine Learning; Artificial Intelligence;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

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