Credit scoring using the clustered support vector machine

• This study introduces the use of the clustered support vector machine (CSVM) for credit scoring.
• The CSVM has been shown to relax size constraints while remaining highly accurate.
• The results suggest that the CSVM is a useful alternative to kernel SVM approaches when training datasets get large.

This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally.