An Analysis of Support Vector Machines for Credit Risk Modeling

Authors:
Murat Emre Kaya;Fikret Gurgen;Nesrin Okay
Affiliations:
Risk Analytics Unit, Mashreqbank, 1250, Dubai, UAE;Department of Computer Engineering, Bogazici University, 34342, Istanbul, Turkey;Department of Management, Bogazici University, 34342, Istanbul, Turkey
Venue:
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Year:
2008

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Abstract

In this study, we analyze the ability of support vector machines (SVM) for credit risk modeling from two different aspects: credit classification and estimation of probability of default values. Firstly, we compare the credit classification performance of SVM with the widely used technique of logistic regression. Then we propose a cascaded model based on SVM in order to obtain a better credit classification accuracy. Finally, we propose a methodology for SVM to estimate the probability of default values for borrowers. We furthermore discuss the advantages and disadvantages of SVM for credit risk modeling.