An Application of Fuzzy Canonical Correlation and Fuzzy DEA for Ranking Bank Branches

Abstract

Performance evaluation and efficiency analysis of economic units are of great importance. Measuring the efficiency of the banking industry has been one of the most interesting areas of research for the past few years. There are literally various techniques for measuring the relative performance of similar units such as banks including Data Envelopment Analysis. Data Envelopment Analysis method is a fact based mathematical programming which is used to measure and analyze the efficiency of decision making units. In addition, the canonical correlation analysis technique is one of the multivariate statistical methods to analyze and rank units. However, the observed values of the input and output data in real- world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. In this paper, a canonical correlation analysis model is proposed using fuzzy numbers. This model can be used to rank the fuzzy efficiency of decision making units according to their efficiency values. This study aims to evaluate and rank the performance of MELLI bank branches based on FUZZY CCA and FUZZY DEA techniques. We utilized the non-parametric Friedman test to compare the results from the two methods. Statistic test results indicated that the full ranking of the fuzzy canonical correlation analysis is consistent with results from fuzzy data envelopment analysis method.