Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Axis 4: Reject Inference Methods in Credit Scoring: a rational review

Participants : Christophe Biernacki, Vincent Vandewalle, Adrien Ehrhardt.

The granting process of all credit institutions is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability also called score is learnt based on a dataset in which rejected applicants are de facto excluded. This implies that the population on which the score is used will be different from the learning population. Thus, this biased learning can have consequences on the scorecard's relevance. Many methods dubbed reject inference have been developed in order to try to exploit the data available from the rejected applicants to build the score. However most of these methods are considered from an empirical point of view, and there is some lack of formalization of the assumptions that are really made, and of the theoretical properties that can be expected. We propose a formalisation of these usually hidden assumptions for some of the most common reject inference methods, and we discuss the improvement that can be expected. These conclusions are illustrated on simulated data and on real data from Credit Agricole Consumer Finance (CACF), a major European loan issuer. Adrien Ehrhardt defended his PhD thesis on this topic this year [13]. A preprint is being finalized to be submitted to an international journal or conference.

This is a joint work with Philippe Heinrich from Université de Lille.