The New Method For Credit Customer Selecting By Integration of A^2 and Data Envelopment Analysis (A^2_Dea)

This paper develops a decision support tool using an a^2 method and data envelopment analysis (dea) approach (a^2-dea). this new method is applied for the bank credit customer selection problem and credit scoring as a pilot survey at export development bank of iran. the proposed method has led to fewer calculations, faster and more accurate decision making, less complexity, and ability to analyze many scenarios with only one or a few judgments of decision makers while the effect of the subjective opinion of one single decision maker will be avoided. this proposed method is compared with adaptive analytical hierarchy process approach, which is suggested by lin et al., in 2008, and it is named a^3. an illustrative example demonstrates the implementation of the proposed approach. this example demonstrates how this approach can avoid the main drawback of the current method, and more importantly, can deal with the credit customer selection more convincingly and persuasively. the implementation results show that this method is significantly valid for ranking credit customers. comparison of methods shows that although a^3 have benefits, it also suffers from limitations, which can be avoided by the a^2-dea model, also improves the time and cost needed for implementing in comparison.