A Comprehensive Trust Evaluation Model for Financial Service Providers Using Fuzzy Inference Systems

This paper presents a hybrid trust evaluation model for financial service providers based on fuzzy inference systems (FIS) and machine learning methods. The proposed model aggregates FSLA compliance measures, operational performance information, and user feedback to calculate dynamic, multidimensional trust scores. The model utilizes both the strengths of fuzzy logic in handling uncertainty and ambiguity, as well as the predictive power and real-time robustness of machine learning. The effectiveness of this hybrid method in overcoming the constraints of existing trust evaluation frameworks was demonstrated by the results, such as their static style, reliance on subjective evaluations, and lack of integration across crucial variables. Moreover, the quantitative evaluation indicated good accuracy, precision, and recall, highlighting the model’s reliability and practical application. The suggested framework can evolve into a more versatile and powerful instrument for trust evaluation, thereby enhancing its contributions to the financial industry and beyond.