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...

We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER...

This study proposes a theoretical and practical reflection on the use of machine learning methods in the context of the Internal Ratings Based (IRB) approach to banks' capital requirements. While machine learning is still rarely used in the regulatory domain (IRB, IFRS 9, stress tests), recent...