Abstract
The large-scale proliferation of China’s new type of agricultural entities has given rise to a higher demand for funds. Farmers have insufficient effective collateral, which makes it difficult for them to obtain sufficient loans. Chinese financial institutions have developed a biological asset mortgage loan business to cope with this situation. China has not considered biological mortgages but has been using real estate and asset mortgage models with strong realizability. This innovative financial business has achieved positive results since it was attempted, but it also faces many risks. It is very important to comprehensively and accurately consider the risk factors of biological asset mortgage loans. Based on 1249 production and operation data samples of new agricultural entities in Zhejiang, Henan, and Shandong provinces, this study constructs an XGBoost model for empirical analysis and compares it with logical regression, support vector machine, and random forest algorithms to obtain the optimal model and feature importance value. According to the characteristic importance value, a biological asset mortgage loan risk assessment system with 4 primary indicators and 20 secondary indicators is established, which can effectively identify the biological asset mortgage loan risk of new agricultural entities.
Highlights
As China’s agricultural production increases in scale, intensification, and specialization, the process of agricultural modernization is gradually accelerating, and the ranks of new agricultural management entities are gradually expanding
Subtype variables should be numerically processed when using machine learning modeling, and continuous variables should be normalized when using logistic regression and support vector machine modeling to avoid inaccurate results caused by large data values. erefore, this study performs one-hot encoding on the categorical data and performs missing values, outliers, and normalization at the same time
“whether to buy insurance” is an important indicator to measure the risk of biological asset mortgage loans for new agricultural operators, and this indicator can be used as the “threshold” for financial institutions to issue biological asset mortgage loans
Summary
As China’s agricultural production increases in scale, intensification, and specialization, the process of agricultural modernization is gradually accelerating, and the ranks of new agricultural management entities are gradually expanding. As the magnitude and nature of Chinese agriculture are different from those of other countries, there is less research on establishing a risk assessment system for biological asset mortgage loans in at present, and Chinese scholars often use analytic hierarchy process and fuzzy comprehensive evaluation methods for analysis. In order to promote the development of new agricultural business entities, financial institutions should innovate financial products and service methods and actively carry out pilot projects such as forest right mortgage, land contractual management right mortgage, and biological asset mortgage loan, in order to activate farmers’ assets and form a multichannel financial support system [5, 6]. Is study further establishes an evaluation system that can accurately identify the risk of biological asset mortgage loans of new agricultural business entities Considering the imbalance of sample data, this study uses the XGBoost algorithm to fit the possible risk points of biological asset mortgage loans, and compares the classification performance with other algorithms. is study further establishes an evaluation system that can accurately identify the risk of biological asset mortgage loans of new agricultural business entities
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