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Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods

Author

Listed:
  • Yu Xia

    (School of Management, Henan University of Technology, Zhengzhou 450001, China)

  • Ta Xu

    (School of Management, Henan University of Technology, Zhengzhou 450001, China)

  • Ming-Xia Wei

    (School of Management, Henan University of Technology, Zhengzhou 450001, China)

  • Zhen-Ke Wei

    (Dyson School of Applied Economics and Management, Cornell University, New York, NY 14850, USA)

  • Lian-Jie Tang

    (School of Management, Henan University of Technology, Zhengzhou 450001, China)

Abstract

Supply chain finance is an effective way to solve the financial problems of small and medium-sized manufacturing enterprises, and the assessment of credit risk is one of the key issues in supply chain financing. However, traditional credit risk assessment models cannot truly reflect the credit status of financing companies. In recent years, scholars working in this field have proposed using machine learning methods to predict the credit risk of supply chain enterprises, achieving good results. Nonetheless, there is no consensus on which approach is the most suitable for manufacturing companies. This study took small and medium-sized manufacturing enterprises as the research object, selected risk evaluation indicators according to the characteristics of the small and medium-sized manufacturing enterprises, and built a credit risk evaluation system. On this basis, we selected SMEs on China’s stock market from 2015 to 2020 as the sample data and evaluated corporate credit risk based on four commonly used machine learning algorithms. Then, combined with the evaluation results, a partial dependence plot method was used to visually analyze the important indicators. The results showed that a credit risk evaluation system for supply chain finance for manufacturing SMEs could be composed of the profile of the financing companies, the asset status of the financing companies, the profile of the core companies, and the operation of supply chains. The use of a random forest algorithm made it possible to more accurately assess the credit risk of manufacturing supply chain finance. Since the impacts of different indicators on the evaluation results were quite different, supply chain enterprises and financial service institutions should formulate corresponding strategies according to specific situations.

Suggested Citation

  • Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1087-:d:1027537
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    References listed on IDEAS

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    1. Niinimäki, J.-P., 2011. "Nominal and true cost of loan collateral," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2782-2790, October.
    2. Panos Kouvelis & Wenhui Zhao, 2018. "Who Should Finance the Supply Chain? Impact of Credit Ratings on Supply Chain Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 20(1), pages 19-35, February.
    3. WeiMing Mou & Wing-Keung Wong & Michael McAleer, 2018. "Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    4. Xu, Xinhan & Chen, Xiangfeng & Jia, Fu & Brown, Steve & Gong, Yu & Xu, Yifan, 2018. "Supply chain finance: A systematic literature review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 204(C), pages 160-173.
    5. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    6. Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).
    7. Mou, W.M. & Wong, W.-K. & McAleer, M.J., 2018. "Financial Credit Risk and Core Enterprise Supply Chains," Econometric Institute Research Papers EI2018-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Çömez-Dolgan, Nagihan & Tanyeri, Başak, 2015. "Inventory performance with pooling: Evidence from mergers and acquisitions," International Journal of Production Economics, Elsevier, vol. 168(C), pages 331-339.
    9. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    10. Wang, Zhiqiang & Wang, Qiang & Lai, Yin & Liang, Chaojie, 2020. "Drivers and outcomes of supply chain finance adoption: An empirical investigation in China," International Journal of Production Economics, Elsevier, vol. 220(C).
    11. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    12. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    13. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    14. Wetzel, Philipp & Hofmann, Erik, 2019. "Supply chain finance, financial constraints and corporate performance: An explorative network analysis and future research agenda," International Journal of Production Economics, Elsevier, vol. 216(C), pages 364-383.
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