providing an intelligent credit risk management system of the bank based on the macroeconomic indicators in the country's stock exchange banks

This study is focused on establishing an intelligent credit risk management system for banks, incorporating macroeconomic indicators through a combined methodology of econometrics and artificial intelligence. in addition to reviewing scientific documents and reports, panel data from annual reports and datasets of stock exchange banks are analyzed within the matlab programming environment. one of the most significant findings of this research is the approach, which is based on calculations derived from the garch economic model. in this approach, the input values for the component inflation rate factor (a4) carry a weight of 0.943734, equivalent to the high h membership function. the component bank deposit rate factor (b4) holds a weight of 0.959346, also equivalent to the high h membership function. the component unemployment rate factor (a3) is weighted at 0.990343, again corresponding to the high h membership function. similarly, the component exchange rate factor (b2) bears a weight of 0.990413, denoting the high h membership function. lastly, the component gdp growth rate factor (a1) is weighted at 0.959256, corresponding to the high h membership function. in essence, these results indicate that the total score amounts to 5.46 within a range of 6, positioning the target variable at the 91st level, which is the fifth level of the system output, signifying an excellent performance.