مجله راهبرد مدیریت مالی
سال هشتم شماره 2 (پیاپی 29، تابستان 1399)

Providing Optimized Banks Resource Allocation by Emphasizing on the Role of Risk management (Total Criteria Approach and Sequential Unconstrained Optimization Technique) Most important activities of banking system is capital and storage attract and then its distribution in form of loans and facilities at micro and macro- economic level. If this activity done on the base of correct plan and macroeconomic object framework, could cause different economic activity improvement and economic development. Between these issues mustn’t forget the role of risk in facilities and loans. So in this research we try to providing multi objective resource allocation model that emphasize on risk management. We present mathematical model with minimizing two objects: minimizing cost of deposits and maximizing facilities profit. For solving this model we use total criteria method by degree 2 that minimized sum squared variance. Finally we use from Sequential Unconstrained optimization Technique because of nonlinear characteristic that exist in degree 2 of total criteria method.

One of the most important issues faced by banks and financial institutions is the issue of credit risk. The significant amount of deferred bank claims around the world indicates the importance of this issue and the need to pay attention to it. So far, many efforts have been made to provide an effective model for evaluating and classification credit applicants as accurately as possible. In this regard, the present study attempts to provide a new approach for assessing the credit risk of bank customers. The support vector machine(SVM) method is combined as the main classifier of banking customers, with a feature selection method called the Binary Ant Colony Optimization Algorithm(BACO-SVM). In order to demonstrate the effectiveness of the proposed method, we used data from 85 companies from legal recipients of facilities of an Iranian bank in a 5 year interval (1393-1893) along with 16 characteristics related to each of them. The results of the BACO-SVM method have been compared with the PSO-SVM, GA-SVM, and SVM method. The results of the research indicated that BACO-SVM model has better performance in assessing credit risk rather than other methods. As the result, using the BACO-SVM method, we classify customers into two groups of good and bad account customers. Finally, in order to increase the flexibility in decision making, we will rank our good account customers with the VIKOR method. This rating will lead to a more accurate assessment of the credit risk situation of good account applicants.