Designing an Expert System to Manage Banking Resources and Expenditures

Assets and debts management includes a series of technical tools and methods that contemplate risk control and asset value creation for shareholders. Since one of the main tasks of banks' financial management is asset and liability management, therefore, in order to increase their profitability, they use asset and debt management techniques to control risk, in order to minimize the losses caused by their transactions. In this research, to solve this challenge, an expert system was designed. The study period of this research was between August 2017 and December 2018. First, data mining techniques were used to predict the bank's credit risk due to the data of 311 legal clients of the Tosee Saderat Bank, with 44 Attributes along with a labeled variable (background of fulfillment of commitment). Data mining was done in two phases, the first phase was data mining in Python language and the second phase was done with Rapid Miner software and creating a decision tree, and finally the finalized rules for measuring the bank's credit risk. Then, the variables and proportions affecting the balance of assets and liabilities of the balance sheet items were collected by interviewing an expert and ranked by five experts in terms of the importance of scoring, and then weighting was done by ARRAS method. High-weight variables (subsystem I) entered the expert system along with the variables in the rules of the decision tree (subsystem II). Finally, the validation of the expert system was obtained. In this way, the index of the ratio of working capital to total assets and the history of working with the bank are the most important variables in validation methods. According to the obtained results, if the credit risk is high, but the balance sheet items are in the optimum, the resources and expenses will be in balance,....