ارزیابی ریسک اعتباری شرکت‌های وام گیرنده از بانک با استفاده از تحلیل سلسله مراتبی فازی و شبکه عصبی ترکیبی درجه بالا

  Banks as financial institutions must estimate the credit risk of their debtors. This is the basis of pricing a loan, determining appropriate interest rates and determining the mortgage required to each borrower. Since the continuity of bank activities largely depends on the amount of credit losses in a particular period, banks should consider the credit quality of their loan portfolio as a collection of debts.   In this paper, the calculation of credit risk of corporates applying for loans has been investigated. Using fuzzy analytic hierarchy process, effective criteria for credit risk have been analyzed.   The neural network is used to extract an open box model that describes the relationship between effective criteria and the credit risk of the companies who apply for a loan. Neural network model has been run with historical data. Observations have been based on 174 corporate who had taken out a loan from a major Iranian bank named Mellat (All loans had been made during 2005 to 2008). The output of the model can predict credit risk of a corporate by at least 84% accuracy .