ISSN: 2225-8329
Open access
The aim of this paper is to examine the efficiency of two credit risk modeling (CRM) to predict the credit risk of commercial Iranian banks: (1) Logistic regression model (LRM); (2) Artificial neural networks (ANNs). The calculations have been done by using SPSS and MATLAB software. Number of samples was 316 and 5 dependent variables. The results showed that, artificial neural network is more proper to identify bad customers in commercial bank. The major contribution of this paper is specifying the most important determinants for rating of customers in Iran’s banking sector.
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In-Text Citation: (Karimi, 2014)
To Cite this Article: Karimi, A. (2014). Credit Risk Modeling for Commercial Banks. International Journal of Academic Research in Accounting Finance and Management Sciences, 4(3), 254–261.
Copyright: © 2014 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
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