Research On Farmers’ Credit Assessment Based On Agricultural Industry Chain Financing

In the context of accelerating the development of agricultural modernization and promoting the realization of rural revitalization,credit assessment of farmers is a key element in the development of agricultural industry chain financing model.Farmers’ credit rating based on agricultural industry chain financing can effectively solve the problems of information asymmetry and high information collection cost between financial institutions and farmers.With the development of agricultural industry chain financing model in various places,scholars have started to explore the operation mechanism and model innovation of agricultural industry chain financing,but few scholars have been involved in the research on credit assessment of farmers under agricultural industry chain financing model.Therefore,this paper takes farmers participating in agricultural industry chain financing in Fuping County,Shaanxi Province as the research object,constructs a farmer credit assessment index system,calculates farmer credit scores,and further tests the accuracy of machine learning related methods for farmer credit assessment.The use of machine learning and other methods for credit assessment of farmers can optimize the assessment mechanism of farmers’ credit and improve the accuracy and objectivity of farmers’ credit assessment.It has important theoretical and practical significance for promoting the development of agricultural industry chain financing model and farmer credit assessment.Based on relevant domestic and international literature,this paper presents a descriptive statistical analysis of farmers’ participation in agricultural industry chains based on theories related to agricultural industry chain financing and credit evaluation,using relevant data obtained from field research in Fuping County,Shaanxi Province in 2022.The hierarchical analysis method was used to construct a farmer credit evaluation index system based on agricultural industry chain financing;support vector machine(SVM)and deep neural network(DNN)were used to empirically analyze the sample farmers,and the accuracy of machine learning related methods for farmer credit evaluation was compared and analyzed,and the following research conclusions were drawn.(1)Based on the analysis of the operation mechanism and characteristics of agro-industrial chain financing mode,this paper constructs a farmer credit assessment index system based on agro-industrial chain financing.A four-level index system is mainly constructed,including the target level,i.e.,the credit of farmers based on agricultural industry chain,the first-level criterion level: the credit characteristics of farmers,the operation status of agricultural industry chain and the credit situation of agricultural industry chain;the second-level criterion level: 12 influencing factors such as personal characteristics of farmers,the operation status of farmers,the stability of industry chain and market risk;and the program level has 32 influencing factors.(2)On the basis of the constructed index system,the weight values of each indicator on the influence of the decision target farmers’ credit were determined by using hierarchical analysis.It was found that the program level indicators: source of repayment funds,knowledge of agro-industrial chain financing,loan amount,total annual household income,frequency of post-loan follow-up checks,and knowledge of loan policies of financial institutions had larger weight coefficients and greater influence on the credit of farmers.Using the weighted average method,the credit score of farmers is calculated on the basis of the derived indicator weights to determine the credit rating of farmers.(3)The empirical tests of support vector machine(SVM)and deep neural network(DNN)on farmers’ credit found that the prediction of farmers’ credit by support vector machine(SVM)is better in the case of smaller sample size.Finally,this paper puts forward targeted suggestions: to innovate the financing mode of agricultural industry chain from multiple subjects such as financial institutions,enterprises and governments;to improve the credit assessment system of farmers in terms of constructing index systems and innovative assessment models;and to establish a risk management mechanism based on agricultural industry chain.