IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1087-d1027537.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1087/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/2/1087/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Niinimäki, J.-P., 2011. "Nominal and true cost of loan collateral," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2782-2790, October.
    4. 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.
    5. 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.
    6. 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.
    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. 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).
    10. 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..
    11. 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.
    12. 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.
    13. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    2. Rasa Kanapickiene & Renatas Spicas, 2019. "Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania," Risks, MDPI, vol. 7(2), pages 1-23, June.
    3. Anh Huu Nguyen & Thinh Gia Hoang & Vu Minh Ngo & Loan Quynh Thi Nguyen & Huan Huu Nguyen, 2023. "Sustainability-oriented supply chain finance in Vietnam: insights from multiple case studies," Operations Management Research, Springer, vol. 16(1), pages 259-279, March.
    4. Meiyan Li & Yingjun Fu, 2022. "Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    5. Judit Oláh & Sándor Kovács & Zuzana Virglerova & Zoltán Lakner & Maria Kovacova & József Popp, 2019. "Analysis and Comparison of Economic and Financial Risk Sources in SMEs of the Visegrad Group and Serbia," Sustainability, MDPI, vol. 11(7), pages 1-19, March.
    6. Yan, Nina & Jin, Xuyu & Zhong, Hechen & Xu, Xun, 2020. "Loss-averse retailers’ financial offerings to capital-constrained suppliers: loan vs. investment," International Journal of Production Economics, Elsevier, vol. 227(C).
    7. Zhu, You & Zhou, Li & Xie, Chi & Wang, Gang-Jin & Nguyen, Truong V., 2019. "Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 22-33.
    8. Zhu, Xiaoyan & Cao, Yunzhi & Wu, Jinwei & Liu, He & Bei, Xiaoqiang, 2022. "Optimum operational schedule and accounts receivable financing in a production supply chain considering hierarchical industrial status and uncertain yield," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1142-1154.
    9. Raffaella Calabrese, 2012. "Improving Classifier Performance Assessment of Credit Scoring Models," Working Papers 201204, Geary Institute, University College Dublin.
    10. Jonathan K. Budd & Peter G. Taylor, 2015. "Calculating optimal limits for transacting credit card customers," Papers 1506.05376, arXiv.org, revised Aug 2015.
    11. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    12. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    13. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    14. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    15. K Rajaratnam & P Beling & G Overstreet, 2010. "Scoring decisions in the context of economic uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 421-429, March.
    16. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    17. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    18. Dmytro Kovalenko & Olga Afanasieva & Nani Zabuta & Tetiana Boiko & Rosen Rosenov Baltov, 2021. "Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies," JRFM, MDPI, vol. 14(5), pages 1-20, May.
    19. Faranak Emtehani & Nasim Nahavandi & Farimah Mokhatab Rafiei, 2021. "A joint inventory–finance model for coordinating a capital-constrained supply chain with financing limitations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-39, December.
    20. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1087-:d:1027537. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.