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The Fairness of Credit Scoring Models

Author

Listed:
  • Christophe Hurlin

    (UO - Université d'Orléans)

  • Christophe Perignon

    (HEC Paris - Ecole des Hautes Etudes Commerciales)

  • Sébastien Saurin

    (UO - Université d'Orléans)

Abstract

In credit markets, screening algorithms discriminate between good-type and bad-type borrowers. This is their raison d'être. However, by doing so, they also often discriminate between individuals sharing a protected attribute (e.g. gender, age, race) and the rest of the population. In this paper, we show how to test (1) whether there exists a statistical significant difference in terms of rejection rates or interest rates, called lack of fairness, between protected and unprotected groups and (2) whether this difference is only due to credit worthiness. When condition (2) is not met, the screening algorithm does not comply with the fair-lending principle and can be qualified as illegal. Our framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, and improved for the benefit of protected groups.

Suggested Citation

  • Christophe Hurlin & Christophe Perignon & Sébastien Saurin, 2021. "The Fairness of Credit Scoring Models," Working Papers hal-03501452, HAL.
    • Handle: RePEc:hal:wpaper:hal-03501452
    DOI: 10.2139/ssrn.3785882
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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. 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.
    4. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska, 2025. "Efficiency versus fairness in link recommendation algorithms," Documents de travail du Centre d'Economie de la Sorbonne 25001, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    5. 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.

    More about this item

    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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