Credit Risk Modeling for Commercial Banks
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.
Year of publication: |
2014
|
---|---|
Authors: | Karimi, Asrin |
Published in: |
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. 4.2014, 3, p. 187-192
|
Publisher: |
Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences |
Subject: | Credit risk | modeling | artificial neural network | multiple regression | loan |
Saved in:
freely available
Saved in favorites
Similar items by subject
-
Loan decision models for the Jordanian commercial banks
Eletter, Shorouq Fathi, (2017)
-
Pan, Yuchen, (2021)
-
Rane, Santosh B., (2018)
- More ...
Similar items by person
-
Evaluation of the Credit Risk with Statistical analysis
Karimi, Asrin, (2014)
- More ...