Credit scoring, augmentation and lean models

J Banasik () and J Crook ()
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J Banasik: University of Edinburgh
J Crook: University of Edinburgh

Journal of the Operational Research Society, 2005, vol. 56, issue 9, 1072-1081

Abstract: Abstract If a credit scoring model is built using only applicants who have been previously accepted for credit such a non-random sample selection may produce bias in the estimated model parameters and accordingly the model's predictions of repayment performance may not be optimal. Previous empirical research suggests that omission of rejected applicants has a detrimental impact on model estimation and prediction. This paper explores the extent to which, given the previous cutoff score applied to decide on accepted applicants, the number of included variables influences the efficacy of a commonly used reject inference technique, reweighting. The analysis benefits from the availability of a rare sample, where virtually no applicant was denied credit. The general indication is that the efficacy of reject inference is little influenced by either model leanness or interaction between model leanness and the rejection rate that determined the sample. However, there remains some hint that very lean models may benefit from reject inference where modelling is conducted on data characterized by a very high rate of applicant rejection.

Keywords: reject inference; credit scoring; augmentation (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (8)

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DOI: 10.1057/palgrave.jors.2602017

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