Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM)

Shahlaii Moghadam, A; Shalbafzadeh, A; Saraee, MH

Abstract

Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps and Support Vector Machine are two techniques of data mining which are used in different applications of businesses. In this paper, we use descriptive variables in literatures and criteria which effect on credit of customers in Iran financial institutions. We will evaluate these variables with Multi Criteria Decision Making (MCDM) and take into account the psychological and sociology viewpoints of experts. Next We apply and compare SVM method against SOM method on the credit database. For comparing these two methods we use coincidence matrix and the Type I and Type II errors. We show that they are competitive and most significant in determining the risk of default on bank customers.

Citation

Shahlaii Moghadam, A., Shalbafzadeh, A., & Saraee, M. (2009, December). Better classifiers for credit scoring : a comparison study between self organizing maps (SOM) and support vector machine (SVM). Presented at 3rd International Conference on Communications and information technology, Vouliagmeni, Athens, Greece

• Presentation Conference Type: Other
• Conference Name: 3rd International Conference on Communications and information technology
• Conference Location: Vouliagmeni, Athens, Greece
• Start Date: Dec 29, 2009
• End Date: Dec 31, 2009
• Publication Date: Dec 31, 2009
• Deposit Date: Aug 20, 2018
• Publisher URL: https://dl.acm.org/citation.cfm?id=1736147
• Related Public URLs: https://dl.acm.org/citation.cfm?id=1736135&picked=prox
• Additional Information: Additional Information : Proceedings ISBN: 9789604741465; Event Type : Conference