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Credit Risk Modeling for Commercial Banks

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  • Asrin Karimi

Abstract

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.

Suggested Citation

  • Asrin Karimi, 2014. "Credit Risk Modeling for Commercial Banks," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(3), pages 187-192, July.
    • Handle: RePEc:hur:ijaraf:v:4:y:2014:i:3:p:187-192
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    References listed on IDEAS

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    1. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    2. Maximilian Hall & Dadang Muljawan & Lolita Moorena, 2009. "Using the artificial neural network to assess bank credit risk: a case study of Indonesia," Applied Financial Economics, Taylor & Francis Journals, vol. 19(22), pages 1825-1846.
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    Cited by:

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