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A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance

Author

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  • Lang Zhang

    (School of Economics and Administration, Xi’an University of Technology)

  • Haiqing Hu

    (School of Economics and Administration, Xi’an University of Technology)

  • Dan Zhang

    (School of Economics and Administration, Xi’an University of Technology)

Abstract

Background Supply chain finance (SCF) is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital. As a systematic arrangement, SCF utilizes the authenticity of the trade between (SMEs) and their “counterparties”, which are usually the leading enterprises in their supply chains. Because in these arrangements the leading enterprises are the guarantors for the SMEs, the credit levels of such counterparties are becoming important factors of concern to financial institutions’ risk management (i.e., commercial banks offering SCF services). Thus, these institutions need to assess the credit risks of the SMEs from a view of the supply chain, rather than only assessing an SME’s repayment ability. The aim of this paper is to research credit risk assessment models for SCF. Methods We establish an index system for credit risk assessment, adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain. Furthermore, We conducted two credit risk assessment models based on support vector machine (SVM) technique and BP neural network respectly. Results (1) The SCF credit risk assessment index system designed in this paper, which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises, can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not. Therefore, more SMEs can obtain loans from banks through SCF. (2) The SCF credit risk assessment model based on SVM is of good generalization ability and robustness, which is more effective than BP neural network assessment model. Hence, Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model, which can alleviate credit rationing on SMEs. Conclusions (1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise, and thereby improve the credit rating status in the process of SME financing. (2)By analyzing and comparing the empirical results, we find that the SVM assessment model, on evaluating the SME credit risk, is more effective than the BP neural network assessment model. This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs. (3)Therefore, the SCF credit risk assessment index system and the assessment model based on SVM, is the optimal combination for commercial banks to use to evaluate SMEs’ credit risk.

Suggested Citation

  • Lang Zhang & Haiqing Hu & Dan Zhang, 2015. "A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-21, December.
  • Handle: RePEc:spr:fininn:v:1:y:2015:i:1:d:10.1186_s40854-015-0014-5
    DOI: 10.1186/s40854-015-0014-5
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    References listed on IDEAS

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    1. Altman, Edward I., 1980. "Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(4), pages 813-832, November.
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    4. Kong, Lingxuan & Zheng, Ge & Brintrup, Alexandra, 2024. "A federated machine learning approach for order-level risk prediction in Supply Chain Financing," International Journal of Production Economics, Elsevier, vol. 268(C).
    5. Bastien Lextrait, 2021. "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers 2021-25, University of Paris Nanterre, EconomiX.
    6. Lu Xiang & Renyong Hou, 2023. "Research on Innovation Management of Enterprise Supply Chain Digital Platform Based on Blockchain Technology," Sustainability, MDPI, vol. 15(13), pages 1-13, June.
    7. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    8. Paulo Cesar Schotten & Leydiana Sousa Pereira & Danielle Costa Morais, 2022. "Credit granting sorting model for financial organizations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
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    11. Crovini, Chiara & Ossola, Giovanni & Britzelmaier, Bernd, 2021. "How to reconsider risk management in SMEs? An Advanced, Reasoned and Organised Literature Review," European Management Journal, Elsevier, vol. 39(1), pages 118-134.
    12. Karim Amzile & Mohamed Habachi, 2022. "Assessment of Support Vector Machine performance for default prediction and credit rating," Post-Print halshs-03643738, HAL.
    13. Ratri Parida & Manoj Kumar Dash & Anil Kumar & Edmundas Kazimieras Zavadskas & Sunil Luthra & Eyob Mulat‐weldemeskel, 2022. "Evolution of supply chain finance: A comprehensive review and proposed research directions with network clustering analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1343-1369, October.
    14. Parimal Kumar Giri & Sagar S. De & Sachidananda Dehuri & Sung‐Bae Cho, 2021. "Biogeography based optimization for mining rules to assess credit risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 35-51, January.
    15. Xin Liu & Bangxin Zhao & Wenqing He, 2020. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
    16. Chih-Hung Hsu & Ru-Yue Yu & An-Yuan Chang & Wan-Ling Liu & An-Ching Sun, 2022. "Applying Integrated QFD-MCDM Approach to Strengthen Supply Chain Agility for Mitigating Sustainable Risks," Mathematics, MDPI, vol. 10(4), pages 1-41, February.
    17. 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.

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