Journal of the Operational Research Society

, Volume 63, Issue 10, pp 1374–1387

Bound and collapse Bayesian reject inference for credit scoring

  • G G Chen
  • T Åstebro
General Paper

DOI: 10.1057/jors.2011.149

Cite this article as:
Chen, G. & Åstebro, T. J Oper Res Soc (2012) 63: 1374. doi:10.1057/jors.2011.149

Abstract

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.

Keywords

statisticscredit scoringBayesianreject inferencemissing data

Copyright information

© Operational Research Society 2011

Authors and Affiliations

  • G G Chen
    • 1
  • T Åstebro
    • 2
  1. 1.MSCI (Hong Kong) IncHong Kong
  2. 2.HEC ParisJouy-en-JosasFrance