A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods

Abstract

Credit risk evaluation is always the most important factor in determining Customers' credit status in financial institutions. Multi-Attribute Decision-Making (MADM) methods have been widely used in this field. But most of the studies neglect the undeniable impact of time and changes of the credit assessment criteria, their importance and evaluation data over time. On the other hand, developed Dynamic MADM (DMADM) methods often used subjective weighting methods and then applied some aggregation operators to rank alternatives. This paper proposes a new combination of Data Envelopment Analysis (DEA) as a powerful objective weighting method with DMADM as a novel dynamic decision-making method for credit performance evaluation. For this aim, the credit performance criteria were extracted using literature review and experts’ views. Criteria weights were calculated with dynamic DEA common set of weights approach. Then, the applicants are prioritized using five Grey MADM methods (including SAW-G, VIKOR-G, TOPSIS-G, ARAS-G and COPRAS-G). Finally, a new method called Correlation Coefficient and Standard Deviation (CCSD) was used to determine the final aggregated rank. This novel method is applied in order to credit ratings of the clients in the Beekeeping Industry Development Funding Institute IRAN The results indicate that the proposed MADM method, while eliminating the limitations of previous methods, has been able to maintain robustness. Also, the results are highly correlated with the results of previous methods.