Tuesday May 20, 2021 – 12:15-13:45pm – online

The Fairness of Credit Scoring Models

Pr. Chrisoph Hurlin, University of Orléans


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

BIO Pr. Christophe Hurlin

Prof. Christophe Hurlin is leading the Laboratoire d’Économie d’Orléans (LEO). His research interest includez financial econometrics, econometrics, financial risk management, and reproducible research. He has several publications in prestigious academics journals including Journal of Financial Econometrics, JFQA, Review of Finance, European Journal of Operational Research, Journal of Banking and Finance, Journal of Empirical Finance, Management Science and Science