Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: Bank Branches

In today’s economy, the banking industry is very important for economic cycle of each country and provides some quality of services for us. With the advancement in technology and rapidly increasing of complexity of today’s business environment, it has become more competitive than the past so that efficiency analysis in the banking industry attracts much attention in recent years. From many aspects, such analyses at the branch level are more desirable. Evaluating the branchperformance with the purpose of eliminating deficiency can be a crucial issue for branch managers to measure branch efficiency. This work not only can lead to better understanding of bank branch performance, but also give further information to enhance managerial decisions torecognize problematic areas. To achieve this purpose, this study presents an integrated approach based on Data Envelopment Analysis (DEA), Clustering algorithms and Polynomial Pattern Classifier for constructing a classifier to identify class of bank branches. First, the efficiency estimates of individual branches are evaluated by using the DEA approach. Next, when the range and number of classes were identified by experts, the number of clusters is identified by agglomerative hierarchical clustering algorithm based on some statistical methods. Next, we divide our raw data into k clusters By means of self-organizing map (SOM) neural networks. Finally, all clusters are fed into reduced multivariate polynomial model to predict the classes of data.

کلیدواژه ها:

Banking ، Efficiency ، Data Envelopment Analysis ، Data Mining ، Classification