An analysis of support vector machines for credit risk modeling
Frontiers in Artificial Intelligence and Applications, cilt.177, sa.1, ss.25-33, 2008 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 177 Sayı: 1
- Basım Tarihi: 2008
- Doi Numarası: 10.3233/978-1-58603-890-8-25
- Dergi Adı: Frontiers in Artificial Intelligence and Applications
- Derginin Tarandığı İndeksler: Scopus
- Sayfa Sayıları: ss.25-33
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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. © 2008 The authors and IOS Press. All rights reserved.