Bound and Collapse Bayesian Reject Inference for Credit Scoring

33 Pages Posted: 22 Aug 2004 Last revised: 3 Mar 2013

See all articles by Gongyue Chen

Gongyue Chen

University of Waterloo - Department of Management Sciences

Thomas B. Astebro

HEC Paris - Economics and Decision Sciences

Date Written: July 1, 2010

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: Credit scoring, reject inference, missing not at random, Bayesian inference

JEL Classification: C12, C15, C52

Suggested Citation

Chen, Gongyue and Astebro, Thomas B., Bound and Collapse Bayesian Reject Inference for Credit Scoring (July 1, 2010). Available at SSRN: https://ssrn.com/abstract=579001 or http://dx.doi.org/10.2139/ssrn.579001

Gongyue Chen

University of Waterloo - Department of Management Sciences ( email )

Waterloo, Ontario N2L 3G1
Canada

Thomas B. Astebro (Contact Author)

HEC Paris - Economics and Decision Sciences ( email )

Jouy-en-Josas Cedex, 78351
France

HOME PAGE: http://www.hec.edu/Faculty-Research/Faculty-Directory/ASTEBRO-Thomas

Register to save articles to
your library

Register

Paper statistics

Downloads
318
Abstract Views
2,169
rank
95,422
PlumX Metrics