Bound and Collapse Bayesian Reject Inference for Credit Scoring

G. Chen, T. B. ASTEBRO

Journal of the Operational Research Society

October 2012, vol. 63, n°10, pp.1374-1387

Departments: Economics & Decision Sciences, GREGHEC (CNRS)

Keywords: Statistics, Credit scoring, Bayesian, Reject inference, Missing data

Reject inference is a method for inferring how a rejected credit applicant would have behaved had credit been granted. Credit-quality data on rejected applicants are usually missing not at random (MNAR). In order to infer credit-quality data MNAR, we propose a flexible method to generate the probability of missingness within a model-based bound and collapse Bayesian technique. We tested the method's performance relative to traditional reject-inference methods using real data. Results show that our method improves the classification power of credit scoring models under MNAR conditions.