A2022
Title: The fairness of credit scoring models
Authors: Sebastien Saurin - University of Orléans (France) [presenting]
Christophe Hurlin - University of Orleans (France)
Christophe Perignon - HEC Paris (France)
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. We show how (1) to test whether there exists a statistically significant difference between protected and unprotected groups, which is called lack of fairness and (2) to identify the variables responsible for the lack of fairness. We then use these variables to optimize the fairness-performance trade-off. The framework provides guidance on how algorithmic fairness can be monitored by lenders, controlled by their regulators, and improved for the benefit of protected groups.
