This study focuses on providing an intelligent credit risk management system of the bank in the presence of the macroeconomic indicators using a combined methodology of econometrics and artificial intelligence. In addition to the use of scientific documents and reports, the panel data related to the annual reports and datasets of stock exchange banks are analyzed by using the MATLAB programming environment. One of the most important results of the this paper is that the proposed approach has been based on the calculations made with the GARCH economic model in which the input values of the component "Inflation rate factor (A4)” have a weight of 0.943734 (equivalent to the membership function "High H"); the component "rate factor Bank deposit (B4)” has a weight of 0.959346 (equivalent to the "High H" membership function); the component “Unemployment rate factor (A3)” has a weight of 0.990343 (equivalent to the "High H" membership function); the component "Exchange Rate Factor (B2)" has a weight of 0.990413 (equivalent to the membership function "High H"); And the component "GDP growth rate factor (A1)” has a weight of 0.959256 (equivalent to the membership function of "high H"); This means that, 5.46 is within a range of 6, i.e. the target variable is exactly in the 91st position (the fifth level of the system output is excellent).
