The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of SYSTEMS ENGINEERING, APPLICATIONS AND DEVELOPMENT
Transactions ID Number: 19-274
Full Name: Afshin Shahlaii moghadam
Position: Researcher
Age: ON
Sex: Male
Address: Isfahan University of Technology, Isfahan University of Technology, Isfahan, 84156-83111
Country: IRAN
Tel: +989123176103
Tel prefix: +98
Fax:
E-mail address: afshin.shm@gmail.com
Other E-mails: afshin_shahlayi@in.iut.ac.ir
Title of the Paper: Presenting Innovative classifier for credit scoring and comparing with Support Vector Machine (SVM) and Self Organizing Map (SOM)
Authors as they appear in the Paper: Afshin Shahlaii Moghadam , Ali Shalbafzadeh and Mohammad Saraee
Email addresses of all the authors: Afshin.shm@gamil.com,A.shalbafzadeh@ec.iut.ac.ir,M.Saraee@Salford.ac.uk
Number of paper pages: 8
Abstract: Credit scoring has become an increasingly important area for financial institutions. Self Organizing Maps (SOM) and Support Vector Machine(SVM) are two techniques of data mining which are being used in different applications of businesses. In this paper, descriptive variables in literatures and criteria are being used, which affect the credit of customers of the Iranian financial institutions. We begin with evaluating these variables using Multi Criteria Decision Making (MCDM) approach and take into account the psychological and social viewpoints of the experts. Next both SVM and SOM methods are applied to the credit database and the results are compared. To compare 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. In this paper two standard formulated methods and one new algorithm based on SVM and SOM were applied to classi!
fy the customers. The results show that proposed model performs significantly better than standard SOM and SVM. Additionally the proposed model is able to detect bad customers which was set of main purpose this research.
Keywords: Credit Scoring, Support Vector Machine (SVM), Self Organizing Map (SOM)
EXTENSION of the file: .pdf
Special (Invited) Session: Better Classifiers for Credit Scoring: A Comparison Study between Self Organizing Maps (SOM) and Support Vector Machine (SVM)
Organizer of the Session: 101-107
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