Multi‐criteria probabilistic dual hesitant fuzzy group decision making for supply chain finance credit risk assessments

Abstract Supply chain finance is important for resolving small and medium‐sized enterprise financing difficulties and optimizing supply chain capital flows. Therefore, supply chain finance credit risk assessments are key financing indicators for decision makers. However, most current supply chain finance credit risk assessment research has been based on traditional and subjective methods and has focused less on the actual decision‐making requirements. The TODIM method in a fuzzy environment has proven to be a useful tool for multi‐criteria decision‐making as it can better reflect the DMs' psychological characteristics. As probabilistic dual hesitant fuzzy sets can contain more original evaluation information and more comprehensively reflect the DM uncertainties, this paper first proposes a Hamming distance measure in a probabilistic dual hesitant fuzzy environment to compare the relationships between two probabilistic dual hesitant fuzzy elements and prove some of the properties. Then, to fully consider the DMs' risk preferences, a model is proposed to evaluate supply chain finance credit risk that extends the TODIM method to a probabilistic dual hesitant fuzzy environment based on cumulative prospect theory and the proposed Hamming distance. Finally, using improved credit risk assessment criteria, a practical case from Ping An Bank is given to demonstrate the proposed model's practicality, and a comparison given with the classical TODIM and the probabilistic hesitant fuzzy TODIM method to illustrate the effectiveness of the extended method.