A New Method for Clustering in Credit Scoring Problems

Volume 6, Issue 2, pp 97-106 http://dx.doi.org/10.22436/jmcs.06.02.02

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Authors

Mohammad Reza Gholamian - School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. Saber Jahanpour - Department of financial management, Shahid Beheshti University, Tehran, Iran. Seyed Mahdi Sadatrasoul - School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Abstract

Due to the recent financial crisis and regulatory concerns of Basel II, credit risk assessment has become one of the most important topics in the financial risk management. Quantitative credit scoring models are widely used to assess credit risk in financial institutions. In this paper we introduce Time Adaptive self organizing Map Neural Network to cluster creditworthy customers against non credit worthy ones. We test this Neural Network on Australian credit data set and compare the results with other clustering Algorithm’s include K-means, PAM, SOM against different internal and external measures. TASOM has the best performance in clusters customers.

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Keywords

• Credit Scoring
• Banking Industry
• Clustering
• Time adaptive neural network

MSC

•  62H30

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