The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network

The risk assessment methods of agricultural supply chain finance (SCF) are explored to reduce agricultural SCF's credit risks. First, the genetic algorithm (GA) is utilized to adjust and determine the initial weights and thresholds of the backpropagation neural network (BPNN), which assesses the credit risks. Second, for the problem that many factors affect the credit risks and the difficulty in selecting the characteristics, the principle of assessment indicator selection is proposed; the characteristics of these indicators are selected by principal component analysis (PCA). Finally, the case analysis method is utilized to verify the proposed risk assessment method, and an optimal credit risk assessment method is established. The results show that GA-BPNN can accelerate the convergence speed of the BPNN and improve the disadvantage in easily falling into the local minimum of BPNN. The PCA method simplifies the complexity of assessment indicator selection, and the representative indicators in agricultural SCF credit risk assessment are successfully selected. Through verification, it is found that the GA-BPNN algorithm performs well in credit risk prediction of agricultural SCF, and its prediction accuracy and prediction speed are improved. Therefore, the used GA-BPNN has performed well in the credit risk prediction of agricultural SCF, which applies to financial credit risk assessment to reduce the credit risks in agricultural SCF.

Automated summary

This study explores risk assessment methods for agricultural supply chain finance using genetic algorithms and neural networks, which have been shown to effectively reduce credit risks in agricultural SCF through case analysis validation.