Design of User Credit Grading Algorithm in Supply Chain Finance Model

This paper proposes a classification model for user credit ratings under the Supply Chain Finance Model (SCFM) using hierarchical structure modeling. The method incorporates a self-compensation trade financing evaluation technique to determine constraint parameters for credit ratings. It also utilizes multi-domain cloud features and fuzzy data clustering to manage the reliability of hierarchical features in credit ratings, while considering business flow, logistics, information flow, and capital flow. A statistical analysis model is developed within the SCFM framework, combining information content, quality, and other relevant parameters to establish a user credit rating system. To optimize the credit rating process, game equilibrium scheduling is applied to enhance control and improve reliability. Simulation results demonstrate that this approach effectively addresses information asymmetry, improves risk assessment capabilities, and enables real-time credit rating adjustments based on macroeconomic and industrial changes. The proposed method achieves accurate and reliable credit evaluations, supporting better decision-making in supply chain finance.