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

Author

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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)

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.

Suggested Citation

  • Huipo Wang & Meng Liu, 2025. "Credit Risk Assessment of Green Supply Chain Finance for SMEs Based on Multi-Source Information Fusion," Sustainability, MDPI, vol. 17(4), pages 1-21, February.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1590-:d:1591484
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    References listed on IDEAS

    as
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    2. Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
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    4. Yubin Yang & Xuejian Chu & Ruiqi Pang & Feng Liu & Peifang Yang, 2021. "Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China," Sustainability, MDPI, vol. 13(10), pages 1-19, May.
    5. Yixuan Li & Charalampos Stasinakis & Wee Meng Yeo, 2022. "A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance," Forecasting, MDPI, vol. 4(1), pages 1-24, January.
    6. Lv, Chengchao & Bian, Baocheng & Lee, Chien-Chiang & He, Zhiwen, 2021. "Regional gap and the trend of green finance development in China," Energy Economics, Elsevier, vol. 102(C).
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