Machine Learning-driven Group Ranking in Data Envelopment Analysis: Applications in the Banking Sector
Subject Areas :
Keywords: Group Efficiency, Banking Groups, Machine Learning, Neural Network, Data Envelopment Analysis, Ranking,
Abstract :
This paper explores the intersection of Group Ranking in Data Envelopment Analysis (DEA) and the potent capabilities of Machine Learning (ML) within the insurance sector, aiming to redefine group efficiency assessment. While DEA has been a cornerstone for evaluating Decision-Making Units (DMUs), the traditional models fall short in the nuanced insurance sector. To address these limitations, ML is integrated into DEA, enabling more effective DMU ranking. The study includes an empirical application within the banking industry, emphasizing the methodology's relevance and potential to transform the landscape of this industry. Specifically, we evaluate the efficiency of 525 branches of Mellat Bank in Iran, divided into 21 groups, each comprising 25 members. We use the BPNN neural network algorithm to predict the group efficiency score of the 21st group and compare the results with those obtained from the CCR model.