Utility Based Credit Scoring for Banks and Financial Institutions Case Study of a Major Iranian Bank

Volume 13, Issue 4, pp 281-287 http://dx.doi.org/10.22436/jmcs.013.04.01

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Authors

Seyed Mahdi Sadatrasoul - School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. Mohammad Reza Gholamian - School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. Zeynab Hajimohammadi - Amirkabir University of Technology, Department of computer science, Tehran, Iran. Mahdi Hosseini - Advanced Information Systems Research Group, ICT Research Institute of ACECR, Tehran, Iran.

Abstract

Credit scoring mainly distinguishes good customers from the bad ones; therefore it is a classification problem. There are many techniques introduced to solve the problem such as support vector machines, neural networks and rule based classifiers. The main objective of this process is to maximize the profit of bank or financial institute. However these traditional methods of classification seem not to support this objective well. This paper investigates this issue and shows that the best classification model is not necessarily the most profitable model. The applications of the models are shown on an ironing real credit dataset since 2007 to 2012.

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Keywords

• Credit Scoring
• Banking Industry
• Classification
• Utility based data mining.

MSC

•  91B30
•  91B28

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