Deep Analysis of Financial Indicators Affecting Bank Efficiency Using the Monte Carlo Simulation Technique

Abstract

In today’s competitive environment, evaluating bank branch performance plays a crucial role in managerial decision-making. Inefficient branches continuously strive to improve their efficiency, while efficient ones seek to maintain their superior positions. Discriminant Analysis is a common classification method in banking, used to predict the status of new branches based on data from existing ones. However, predictions from this method often involve uncertainty. This study introduces a confidence level metric to determine the status of new branches more accurately. Utilizing sensitivity analysis based on Monte Carlo simulation, the impact of various financial indicators on this confidence level is assessed, identifying key indicators that influence the classification of branches as efficient or inefficient. The results reveal that long-term deposits hold significant importance, whereas variables such as number of personnel, overdue receivables, and Qarz al-Hasna deposits have negligible effects on efficiency classification. These findings provide valuable insights for bank managers in establishing and managing new branches, and enable targeted planning to reform and guide inefficient units towards enhanced efficiency.