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Credit risk analysis is a core research issue in the field of financial risk management. This paper first investigates the analytic hierarchy process (AHP) as a method of measuring index weights for group decision-making (GDM). AHP for group decision-making (AHP-GDM) is then researched and applied, taking into full account the cognitive levels of different experts. Second, the concept of grey relational degree is introduced into the ideal solution of the technique for order of preference by similarity to ideal solution (TOPSIS). This concept fully considers the relative closeness of grey relational degree between alternatives and the ”ideal” solution in order to strengthen their relationship. The AHP-GDM method overcomes the problem of subjectivity in measuring index weights, and the revised TOPSIS (R-TOPSIS) method heightens the effectiveness of assessment results. An illustrative case using data from Chinese listed commercial banks shows that the R-TOPSIS method is more effective than both TOPSIS and grey relational analysis (GRA) in credit risk evaluation. The two improved multi-criteria decision making (MCDM) methods are also applied to empirical research regarding the credit risk analysis of Chinese urban commercial banks. The results indicate the validity and effectiveness of both methods.

Group decision-making using improved multi-criteria decision making methods for credit risk analysis