The Fairness of Credit Scoring Models

46 Pages Posted: 18 Feb 2021 Last revised: 23 May 2022

See all articles by Christophe Hurlin

Christophe Hurlin

University of Orleans

Christophe Pérignon

HEC Paris - Finance Department

Sébastien Saurin

University of Orleans, Laboratoire d'économie d'Orléans, Students

Date Written: May 19, 2022

Abstract

In credit markets, screening algorithms aim to discriminate between good-type and bad-type
borrowers. However, when doing so, they also often discriminate between individuals sharing
a protected attribute (e.g. gender, age, racial origin) and the rest of the population. In this
paper, we show how (1) to test whether there exists a statistically significant difference between
protected and unprotected groups, which we call lack of fairness and (2) to identify the variables
that cause the 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, and improved for the benefit of protected groups.

Keywords: Fairness; Credit scoring models; Discrimination; Machine Learning; Artificial Intelligence

JEL Classification: G21, G29, C10, C38, C55

Suggested Citation

Hurlin, Christophe and Pérignon, Christophe and Saurin, Sébastien, The Fairness of Credit Scoring Models (May 19, 2022). HEC Paris Research Paper No. FIN-2021-1411, Available at SSRN: https://ssrn.com/abstract=3785882 or http://dx.doi.org/10.2139/ssrn.3785882

Christophe Hurlin

University of Orleans ( email )

Université d'Orléans
Rue de Blois B.P. 6739 45
France

Christophe Pérignon (Contact Author)

HEC Paris - Finance Department ( email )

1 rue de la Liberation
Jouy-en-Josas Cedex, 78351
France

Sébastien Saurin

University of Orleans, Laboratoire d'économie d'Orléans, Students ( email )

Orléans cedex 2
France

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