Section: New Results

Reject Inference Methods in Credit Scoring: A Rational Review

Participants : Christophe Biernacki, Adrien Ehrhardt, Vincent Vandewalle.

The granting process of all credit institutions rejects applicants having a low credit score. Developing a scorecard, i.e. a correspondence table between a client’s characteristics and his score, requires a learning dataset in which the target variable good/bad borrower is known. Rejected applicants are de facto excluded from the process. This biased learning population might have deep consequences on the scorecard relevance. Some works, mostly empirical ones, try to exploit rejected applicants in the scorecard building process. This work proposes a rational criterion to evaluate the quality of a scoring model for the existing Reject Inference methods and dig out their implicit mathematical hypotheses. It is shown that, up to now, no such Reject Inference method can guarantee a better credit scorecard. These conclusions are illustrated on simulated and real data from the french branch of Crédit Agricole Consumer Finance (CACF). An early version of this work has been presented as a talk in the national conference [31] and a preprint is being to be finalized.

This is a joint work with Philippe Heinrich of University of Lille and Sébastien Beben of Crédit Agricole Consumer Finance.