Fuzzy expert system to evaluation of bank branch performance using datamining

Fuzzy expert systems are intelligent systems which can be used to obtain better results in evaluating the performance of the banking system. The purpose of this study is to evaluate the performance of bank branches using fuzzy variables beside financial variables. In this study, firstly, the rules of the data were extracted by implementing data mining algorithms on the financial data of branches. In the next step, by obtained rules of financial data and along with fuzzy variables, a fuzzy expert system is designed in order to achieve a system that can comprehensively evaluate the bank branches performance. For designing the considered expert system, nine fuzzy variables such as branch location, customer loyalty, employee satisfaction, customer satisfaction, creativity and innovation, branch appearance, staff appearance, employee stability and also the output of financial rates have been used. Decision tree and C.5 algorithms have been used in order to extract the rules in the branch data. MATLAB fuzzy inference system has been used to design the fuzzy expert system also. The results of the research illustrated the hidden knowledge of the branch data can be extracted via data mining and the performance of bank branches can be evaluated as a comprehensive information system by fuzzy expert systems.