Credit Risk Assessment of Green Supply Chain Finance for SMEs Based on Multi-Source Information Fusion

Huipo Wang () and Meng Liu
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Huipo Wang: School of Management Engineering and Business, Hebei University of Engineering, Handan 056009, China
Meng Liu: School of Management Engineering and Business, Hebei University of Engineering, Handan 056009, China

Sustainability, 2025, vol. 17, issue 4, 1-21

Abstract: As an important pillar of the national economy, the green transformation of SMEs is the key to promoting sustainable economic development. However, SMEs generally face issues such as information opacity and high operational risks, which make it difficult for them to obtain traditional financing support, thereby hindering green development. Green Supply Chain Finance has opened up new financing channels for SMEs, but the accuracy of credit risk evaluation remains a bottleneck that limits its widespread application. This paper constructs a credit risk evaluation index system that integrates multiple sources of information, covering factors such as the situations of SMEs themselves, stakeholder feedback, and expert ratings. It compares and analyzes the performance of the genetic algorithm-optimized random forest model (GA-RF), the BP neural network, the support vector machine, and the logistic regression model in credit risk evaluation. The empirical results indicate that the GA-RF model is significantly better than the other models in terms of accuracy, precision, and F1 score, and has the highest AUC value, making it more effective in identifying credit risk. In addition, the GA-RF model reveals that the asset–liability ratio, the time of establishment, the growth rate of operating revenue, the time of collection of accounts receivable, the return on net assets, and daily shipments are the key indicators affecting the credit risk assessment.

Keywords: green supply chain finance; multi-source information fusion; credit risks; optimized random forest; small- and medium-sized enterprises (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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