A Combined Decision Analysis of MAGDM Approaches for Prioritizing the AI-Driven Digital Financial Services in Promoting Rural Revitalization Quality

The comprehensive advancement of rural revitalization relies heavily on robust support from modern financial services. This research focused on evaluating the quality of artificial intelligence-driven digital financial services in boosting rural revitalization. By analyzing data from smart credit systems and digital advisory platforms, the study aimed to measure effectiveness, identify gaps, and optimize strategies for sustainable and inclusive rural development through technology. The artificial intelligence-driven digital financial service in promoting rural revitalization quality evaluation was viewed as the multiple-attribute group decision-making issue. This study adapted the EDAS technique to operate within single-valued neutrosophic sets environments, creating a practical framework for handling such complex evaluations. The criteria importance through intercriteria correlation method was built to get the attribute's weight. The computational procedures were systematically outlined, and the model was validated through detailed case study. The proposed method's effectiveness was further verified by comparative analysis with existing approaches.