Credit scoring using the clustered support vector machine

Harris, T.

Abstract

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.

Citation

Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42(2), 741-750. https://doi.org/10.1016/j.eswa.2014.08.029

• Journal Article Type: Article
• Acceptance Date: Aug 23, 2014
• Online Publication Date: Sep 6, 2014
• Publication Date: Feb 1, 2015
• Deposit Date: Sep 24, 2015
• Publicly Available Date: Sep 28, 2015
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 2
• Pages: 741-750
• DOI: https://doi.org/10.1016/j.eswa.2014.08.029
• Keywords: Credit risk, Credit scoring, Clustered support vector machine, Support vector machine.
• Public URL: https://durham-repository.worktribe.com/output/1399310