An Optimization Model for Bank Efficiency Forecasting Using Operational Research and Data Driven Methods

Abstract

In today's world economy, the role of banks is essential and crucial for any economy, and they have got a pivotal place in the economic growth processes of society. Moreover, the expansion of banking services in the world through information and communication technology networks and the development of virtual and semi-virtual banks and financial institutions and the presence of the private banking system in the country has created high competition in the banking industry. Competition, durability, producing new services, continuous changes in the needs and demands of customers, have forced banks to editing their strategies in a way as to retain current customers and absorb new customers by providing better services. Therefore, it is necessary to measure the performance of banks by an efficiency evaluation system that provides managers with a comprehensive prospect of the business.

Many researchers have applied data envelopment analysis (DEA) to evaluate the efficiency of banks. However, according to the literature, less attention has been paid to the issue of decision making under uncertainty.

In this research, a flexible and reliable model will be proposed in order to optimize the prediction of the bank's efficiency to deal with uncertainty, and it will survey and analyze various performance and efficiency evaluation methods, and by investigating the process of change and evolution of evaluation methods, especially Combined operations research and data-driven models found their deficiencies and weaknesses so that by analyzing and comparing the efficiency of performance and efficiency evaluation methods, the most suitable model is introduced to optimize the bank's efficiency forecast.

In conclusion, among the types of efficiency evaluation methods with the purpose of increasing the accuracy of forecasting in conditions of uncertainty, the robust data-driven data envelopment analysis model was chosen.