Credit evaluation and rating system for farmers' loans in the context of agricultural supply chain financing based on AHP-ELECTRE III

Abstract

Farmers, often vulnerable within the agricultural supply chain, frequently encounter difficulties accessing and affording loans. This study introduces an innovative credit risk evaluation framework for farmers tailored to the agricultural supply chain. It includes three key aspects: farmers' credit characteristics, the operational status of the agricultural supply chain, and overall credit conditions. Initially, the analytic hierarchy process (AHP) was used to assign weight coefficients to indicators. Then, the Elimination et Choix Traduisant la Réalité III (ELECTRE III) model was employed to determine farmers' credit ratings. To demonstrate the impact of the agricultural supply chain on microfinance, the model's effectiveness was then tested with 398 microfinance survey responses from Fuping County (World Dairy Goat Industry Development Demonstration Zone), Shaanxi Province, China, and its accuracy was further verified using BP neural network analysis. The results demonstrated the model's proficiency in assessing farmers' credit levels within the agricultural supply chain, which can aid in the resolution of various credit assessment and rating challenges. Furthermore, this study offers valuable insights into the integration of multi-criteria decision-making and machine-learning methods.

Keywords

• credit evaluation model
• credit rating
• credit risks
• back propagation neural network
• rural finance