TITLE:
Agricultural Credit Risk Assessment in China Based on the BP and GA-BP Neural Network
AUTHORS:
Fubing Sun
KEYWORDS:
Agricultural Credit, Risk Assessment, Genetic Algorithm, BP Neural Network
JOURNAL NAME:
Modern Economy,
Vol.13 No.6,
June
29,
2022
ABSTRACT: The credit constraint caused by difficult and
expensive loans is a crucial obstacle to agricultural modernization in China.
This is due to the high risk and uncertainty of agricultural production and
operation activities and the high transaction cost and asymmetric information of agricultural credit
activities, which lead to ineffective risk assessment. In this study,comprehensive information on agricultural credit business reports, customer
questionnaires, and loan application forms of
Chinese banks are combined with the characteristics of the agricultural
industry and credit scenarios to develop an innovative
agricultural credit risk assessment index system. The index system is
constructed mainly based on the first repayment source and risk process.
Further, a genetic algorithm optimizes the BP neural
network. The sample data of 1165 agricultural credits collected from
Zhejiang, Jiangsu, Shandong, and Henan provinces are analyzed. The results of
the classification prediction simulation show that this method effectively
reduces the problem of the BP neural network converging to a local minimum and
increases the accuracy and sensitivity correction of data prediction. This overcomes the problem of difficult risk assessment due to
nonstandard and inaccurate agricultural credit data, thus providing
theoretical and practical solutions for improving the efficiency of
agricultural credit risk assessment and control.