Skip to the content

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

Shahlaii Moghada, A, Shalbafzadeh, A and Saraee, M 2009, Better classifiers for credit scoring: a comparison study between self organizing maps (SOM) and support vector machine (SVM) , in: 3rd International Conference on Communications and Information Technology, 29-31 December 2009, Vouliagmeni, Athens, Greece.

[img] Microsoft Word - Accepted Version
Restricted to Repository staff only

Download (143kB) | Request a copy

    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 in 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 2 standard formulated methods and one Innovative algorithm based on SVM and SOM were applied to classify the customers. The results reveal that proposed model performs significantly better than standard SOM and SVM. Additionally the proposed model solves one of the most important challenges in our research which is the ability to detect bad customers.

    Item Type: Conference or Workshop Item (Paper)
    Themes: Media, Digital Technology and the Creative Economy
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: Proceedings of the 3rd International Conference on Communications and Information Technology
    Publisher: WSEAS Press
    Refereed: Yes
    Depositing User: Dr Mo Saraee
    Date Deposited: 26 Oct 2011 14:26
    Last Modified: 20 Aug 2013 18:16
    URI: http://usir.salford.ac.uk/id/eprint/18689

    Actions (login required)

    Edit record (repository staff only)

    No Altmetrics available

    Downloads per month over past year

    View more statistics