A Novel Interval Type-2 Fuzzy CPT-TODIM Method for Multi-criteria Group Decision Making and Its Application to Credit Risk Assessment in Supply Chain Finance

The assessment of credit risk in supply chain finance (SCF) stands as a pivotal procedure in facilitating enterprises to identify appropriate financing solutions, reduce financing costs, enhance capital utilization efficiency, and mitigate the risk of debt default. Multi-criteria group decision-making (MCGDM), a systematic evaluation tool, is widely used for the assessment of both qualitative and quantitative criteria. However, the conventional framework of MCGDM exhibits limitations in addressing scenarios characterized by high uncertainty in risk information, disparity in weights among decision-makers (DMs) and criteria, alongside complex and non-linear risk perception. To address these limitations, this paper introduces an analytical model that integrates Interval Type-2 Fuzzy Sets (IT2FSs), Cumulative Prospect Theory (CPT), and the TODIM (an acronym from Portuguese for Interactive and Multicriteria Decision Making) method to evaluate credit risk in SCF. Firstly, the IT2FSs are utilized to represent high uncertainty in risk assessment information of DMs. Secondly, the Dice Similarity is applied to determine the weights of DMs. Then, we seek to improve the Criterion Importance Through Intercriteria Correlation (CRITIC) method by addressing its limitations and further integrating it with the Bayesian Best-Worst Method (BBWM), offering a robust computational framework of integrated weights for criteria. Finally, the CPT-TODIM method based on IT2FSs is applied in a real case from Ping An Bank. Through rigorous sensitivity and comparative analyses conducted within the real-world context of SCF credit risk assessments, the proposed model's theoretical robustness and practical applicability are emphatically validated.