Optimization of Financing Allocation Using Bunker Model by Neural Network (Case Study: Guilan Bank of Commerce Bank)

Banks are going through a time of crisis. In this situation, many banks are finding an optimal way to invest and make the most of their resources. Customer confidence depends on the profitability of the banks. While banks are struggling to maintain their position, they need to allocate more stringent resources to the right position. But this choice can provide an opportunity for true growth. Tejarat Bank is one of the leading banks in Iranian banking industry. Therefore, the choice of this bank to consider the resource allocation issue can be generalized to other banks as well. This study deals with the issue of resource allocation in the Islamic banking industry. Bunker model is one of the powerful models that has not been addressed in Iran. This model provides conditions for resource allocation in safe areas, considering the resource constraint. Resource allocation is a nonlinear problem and due to the wide range of resources and uses, it is very difficult to solve by well-known mathematical algorithms. Using smart algorithms in such cases is a wise solution to the problem. Artificial neural network is one of the most used intelligent optimization algorithms that is used for modeling. This study was conducted on Guilan Bank of Commerce. The error rate of 1.85 6 10 66 squares in this work indicates that the neural network can provide a precise model of resource allocation based on Bunker algorithm by avoiding deadlock.