Abstract
The literature suggests that the commonly used augmentation method of reject inference achieves no appreciable benefit in the context of logistic and probit regression models. Ranking is not improved and the ability to discern a correct cut-off is undermined. This paper considers the application of augmentation to profit scoring applicants by means of survival analysis and by the Cox proportional hazard model, in particular. This new context involves more elaborate models answering more specific questions such as when will default occur and what will be its precise financial implication. Also considered in this paper is the extent to which the rejection rate is critical in the potential usefulness of reject inference and how augmentation meets that potential. The conclusion is essentially that augmentation achieves negative benefits only and that the scope for reject inference in this context pertains mainly to circumstances where a high proportion of applicants have been rejected.
This is a preview of subscription content, log in via an institution to check access.
References
Andreeva G, Ansell J and Crook J (2007). Modelling profitability using survival combination scores. Eur J Opl Res 183: 1537–1549.
Banasik JL and Crook JN (2007). Reject inference, augmentation, and sample selection. Eur J Opl Res 183: 1582–1594.
Banasik JL, Crook JN and Thomas LC (1999). Not if but when will borrowers default. J Opl Res Soc 50: 1185–1190.
Banasik JL, Crook J and Thomas LC (2003). Sample selection bias in credit scoring models. J Opl Res Soc 54: 822–832.
Bellotti T and Crook J (2008). Credit scoring with macroeconomic variables using survival analysis. J Opl Res Soc, doi:10.1057/jors.2008.130.
Chen G and Astebro T (2006). A maximum likelihood approach for reject inference in credit scoring. Mimeo, Rotman School of Management, University of Toronto.
Crook JN and Banasik JL (2004). Does reject inference really improve the performance of application scoring models? J Bank Financ 28: 857–874.
Feelders AJ (2000). Credit scoring and reject inference with mixture models. Intell Syst in Account, Financ Mngt 9: 1–8.
Fogarty DJ (2006). Multiple imputation as a missing data approach to reject inference on consumer credit scoring, Mimeo, University of Phoenix.
Joannes DN (1993/1994). Reject inference applied to logistic regression for credit scoring. IMA J Math Appl Bus Indust 5: 35–43.
Karakoulas G and Salakhutdinov R (2004). Semi-supervised mixture of experts classification. In: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04). IEEE Computer Society: Washington DC, pp. 138–145.
Little RJ and Rubin DB (1987). Statistical Analysis with Missing Data. John Wiley: New York.
Meester S (2000). Reject inference for credit scoring model development using extrapolation. Mimeo, CIT Group.
Sebastiani P and Ramoni M (2000). Bayesian inference with missing data using bound and collapse. J Comput Graph Stat 9: 779–800.
Smith A and Elkan C (2004). A Bayesian network framework for reject inference. In: Proceedings of The Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association of Computing Machinery: New York, pp. 286–295.
Sohn SY and Shin HW (2006). Reject inference in credit operations based on survival analysis. Expert Syst Appl 31: 26–29.
Stepanova M and Thomas LC (2002). Survival analysis methods for personal loan data. Opns Res 50: 277–289.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Banasik, J., Crook, J. Reject inference in survival analysis by augmentation. J Oper Res Soc 61, 473–485 (2010). https://doi.org/10.1057/jors.2008.180
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1057/jors.2008.180