Agricultural Credit Risk Assessment in China Based on the BP and GA-BP Neural Network
- 1 Zhejiang University of Finance & Economics Dongfang College, Jiaxing, China
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 o f agricultural credit activities, which lead to ineffective risk assessment. In this study, comprehensive information o n 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.
- Dutta, S., & Rating, B. (1988). A Non-Conservative Application of Neural Networks. IEEE International Conference on Neural Networks, 2, 443-450. https://doi.org/10.1109/ICNN.1988.23958
- Huang, Z. G. et al. (2019). Construction and Application of Multi-Source Data Credit Rating Universal Model Stack Framework. The Journal of Quantitative & Technical Economics, 4, 155-168. (In Chinese)
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539
- Meng, B., & Chi, G. T. (2015). Research on Credit Evaluation Model of Farmers’ Microfinance. Modernization of Management, 1, 106-108. (In Chinese)
- Micha, E. et al. (2015). Uptake of Agri-Environmental Scheme in the Less-Favored Areas of Greece: The Role of Corruption and Farmers’ Responses to the Financial Crisis. Land Use Policy, 48, 144-157. https://doi.org/10.1016/j.landusepol.2015.05.016
- Sousa, M. R. (2015). Uptake of Agri-Environmental Schemes in the Less-Favored Areas of Greece: The Role of Corruption and Farmers’ Responses to the Financial Crisis. Land Use Policy, 17, 123-142.