A Maximum Likelihood Approach for Reject Inference in Credit Scoring

33 Pages Posted: 29 Dec 2005 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: November 25, 2006

Abstract

We model reject inference - inferring how a rejected credit applicant would have behaved had it been granted credit - using a maximum likelihood approach within the framework of missing data analysis. Contrary to other methods that impute missing data, this reject inference method embeds the missing data mechanism into model estimation directly. We test performance of three reject inference methods using real default data. Results show our method to be superior and to improve classification power for credit scoring in within-sample tests.

Keywords: reject inference, missing data not at random, maximum likelihood, credit scoring

JEL Classification: C14, C25, C29

Suggested Citation

Chen, Gongyue and Astebro, Thomas B., A Maximum Likelihood Approach for Reject Inference in Credit Scoring (November 25, 2006). Rotman School of Management Working Paper No. 07-05. Available at SSRN: https://ssrn.com/abstract=872541 or http://dx.doi.org/10.2139/ssrn.872541

Gongyue Chen (Contact Author)

University of Waterloo - Department of Management Sciences ( email )

Waterloo, Ontario N2L 3G1
Canada

Thomas B. Astebro

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

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