Credit rating of family farms based on optimal assignment of credit indicators by BP neural network.

Purpose: In order to solve the problems of difficulty in lending to family farms and the lack of credit products, it is necessary to classify the credit rating of family farms and determine the credit risk level of different family farms, so that agriculture-related financial institutions can implement different credit strategies. Design/methodology/approach: A method based on BP neural network model is proposed to measure the weights of credit evaluation indicators of family farms and the linear weighting method and the fuzzy comprehensive evaluation method are used to establish the final credit rating system for family farms. Findings: The empirical results show that the majority of the 246 family farms in Inner Mongolia have a low CC rating. Originality/value: By constructing a sound and reasonable credit rating system for family farms, thus providing an objective evaluation of the credit rating of family farms, the credit granting status of agriculture-related financial institutions will be adapted to the reasonable loan demand status of family farm owners, and the quality and level of their credit approval will be continuously enhanced. [ABSTRACT FROM AUTHOR]