Reducing risk and increasing income of bank customers by Optimizing data mining for the multi-objective model of allocating facilities.

Iranian Commercial banks are always considered as one of the most important institutions active in the money and capital market, due to the economic structure of the country and the lack of development of the capital markets, which makes them in charge of financing the economic sectors of the country. However, these banks are not successful in fulfilling their mission. High level of banks' reserves shows that they do not pay enough attention to risk management and credit portfolio management. There are several models such as linear programming, integer programming, zero and one programming that can provide an optimal combination of the elements that make up the facility basket. However, entering financial information into mathematical planning by considering all conditions is not straightforward to achieve such a goal. In this research, using data mining to optimize the multi-objective model of facility allocation is done using neural network. First, the effective variables were extracted from the bank database and after preparation, the most important features were identified using different algorithms such as random forest algorithm, MARS, and step-wise regression. Then, these methods were compared with each other and the best method was selected. In order to cluster the customers, k-means and k-medoids models have been used. Using different criteria, including the silhouette and the best number of clusters, two clusters have been estimated and customers have been identified in two low-risk and high-risk categories. And finally, by using convolutional neural network, the risk and profit of each customer has been predicted.