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The Assessment of Customers’ Credit Risk in Export Development Bank of Iran

Journal of Insurance and Financial Management

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Title The Assessment of Customers’ Credit Risk in Export Development Bank of Iran
 
Creator Asadi, Abdorreza
Radmehr, Hamid
Shirebrahimi, Shirin
 
Description In recent years, many domestic banks have established risk management offices to manage their performances and customers' risks. The present study was carried out in order to manage, evaluate and reduce credit risks in Export Development Bank of Iran. Required data were collected from financial reports of 806 legal customers who are borrowers as well over 2008 to 2011. Test analyses were done using three methods including the Z-score, Logit regression and Neural Network. It is concluded that the Neural Network with 90.4% of prediction was able to classify the credit risks of customers better than other two models, i.e. Logit regression, with 89.6% and Z-score with 83.2%. In the prediction of default possibility, the efficiency of the Neural Network with 69.8% was higher than Logit model with 65.7% and Z-score with 45.9%. The results also showed that the ratios of free cash flow and assets turnover were considered as the most important ratios by all the three models. On the other hand, the efficiencies of Logit model and Neural Network method are the same calculating the prediction of customers' risks.
 
Publisher Journal of Insurance and Financial Management
 
Contributor —
 
Date 2016-05-17
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://journal-of-insurance-and-financial-management.com/index.php/JIFM/article/view/32
 
Source Journal of Insurance and Financial Management; Vol 1, No 4 (2016): Journal of Insurance and Financial Management
2371-2112
 
Language eng
 
Relation https://journal-of-insurance-and-financial-management.com/index.php/JIFM/article/view/32/pdf
 
Rights Copyright (c) 2016 Abdorreza Asadi, Hamid Radmehr, Shirin Shirebrahimi