Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

The Fairness of Credit Scoring Models

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.
Document type :
Preprints, Working Papers, ...
Complete list of metadata
Contributor : Antoine Haldemann Connect in order to contact the contributor
Submitted on : Thursday, December 23, 2021 - 12:02:38 PM
Last modification on : Friday, December 24, 2021 - 3:28:47 AM

Links full text




Christophe Hurlin, Christophe Perignon, Sébastien Saurin. The Fairness of Credit Scoring Models. 2021. ⟨hal-03501452⟩



Record views