On the suitability of resampling techniques for the class imbalance problem in credit scoring

On the suitability of resampling techniques for the class imbalance problem in credit scoring

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Article ID: iaor20133689
Volume: 64
Issue: 7
Start Page Number: 1060
End Page Number: 1070
Publication Date: Jul 2013
Journal: Journal of the Operational Research Society
Authors: , ,
Keywords: credit scoring, resampling
Abstract:

In real‐life credit scoring applications, the case in which the class of defaulters is under‐represented in comparison with the class of non‐defaulters is a very common situation, but it has still received little attention. The present paper investigates the suitability and performance of several resampling techniques when applied in conjunction with statistical and artificial intelligence prediction models over five real‐world credit data sets, which have artificially been modified to derive different imbalance ratios (proportion of defaulters and non‐defaulters examples). Experimental results demonstrate that the use of resampling methods consistently improves the performance given by the original imbalanced data. Besides, it is also important to note that in general, over‐sampling techniques perform better than any under‐sampling approach.

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