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

• Published in: Computational economics Vol. 60; no. 4; pp. 1269 - 1292
• Main Authors: Wu, Yingli, Li, Xin, Liu, Qingquan, Tong, Guangji
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer; Springer Nature B.V
• Subjects: Agricultural equipment and supplies industry; Agriculture; Algorithms; Artificial neural networks; Back propagation; Back propagation networks; Behavioral/Experimental Economics; Computer Appl. in Social and Behavioral Sciences; Convergence; Credit risk; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Finance; Genetic algorithms; Indicators; Logistics; Math Applications in Computer Science; Neural networks; Operations Research/Decision Theory; Principal components analysis; Risk assessment; Risk reduction; Supply; Supply chains; Thresholds; Verification; Credit risk; Backpropagation neural network; Genetic algorithm; Agricultural supply chain; Principal component analysis
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-021-10137-2

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.