Assessing credit risk rating with Artificial Neural Network method (the Case of an Iranian Bank)

Nowadays¡ Loans in the banking industry has a crucial role in productivity because a substantial portion of the assets of a bank formed though the individuals and companies. So today’s credit risk has known as a biggest contributing factor of failure in the banks and financial institutions. Because of mentioned factors¡ control and management of these risks are necessary.As it mentioned before¡ the aim of this research is a model designed for customers in the areas of Assessing credit risk rating with a synthesized MADM and SOM method. For this purpose¡ the first step is identifying 29 indices that influence credit risk. After that¡ according to expert opinion and the past researches 12 indices was extracted. So the optimal clusters were determined by neural network pattern recognition algorithm. Then customers were categorizing with SOM and K-mean. Finally¡ the relative weight of each indicator of the credit risk assessment was determined.so research show that the customer experience in the economic sectors and account duration has high percentage. Finally¡ some recommendations are offered for future research and suggestions for researchers and practitioners in the field of banking are. At the end¡ some of the limitations mentioned in this research.