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

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 cre...