Using multiple SVM models for unbalanced credit scoring data sets

Abstract

Owing to the huge size of the credit markets, even small improvements in classification accuracy might considerably reduce effective misclassification costs experienced by banks. Support vector machines (SVM) are useful classification methods for credit client scoring. However, the urgent need to further boost classification performance as well as the stability of results in applications leads the machine learning community into developing SVM with multiple kernels and many other combined approaches. Using a data set from a German bank, we first examine the effects of combining a large number of base SVM on classification performance and robustness. The base models are trained on different sets of reduced client characteristics and may also use different kernels. Furthermore, using censored outputs of multiple SVM models leads to more reliable predictions in most cases. But there also remains a credit client subset that seems to be unpredictable. We show that in unbalanced data sets, most common in credit scoring, some minor adjustments may overcome this weakness. We then compare our results to the results obtained earlier with more traditional, single SVM credit scoring models.