IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v268y2024ics0925527323003274.html
   My bibliography  Save this article

A federated machine learning approach for order-level risk prediction in Supply Chain Financing

Author

Listed:
  • Kong, Lingxuan
  • Zheng, Ge
  • Brintrup, Alexandra

Abstract

Supply Chain Financing (SCF) is increasingly utilised as an effective method for optimising cash flows in supply networks. With increased popularity several financial institutions have begun offering SCF to businesses. However various recent scandals have highlighted inefficiencies in the evaluation of risks involved. In this paper we argue this is due to a mismatch between the firm-level features used to evaluate risk and what SCF is given for, which is a particular order. However order-level risk evaluation is difficult as companies do not wish to share their datasets with funders. Furthermore, Small-to-Medium Enterprises (SMEs) themselves may not have enough data to conduct order-level risk evaluation. We propose a Federated Learning (FL) framework to overcome these issues, opening up the possibility for order-level risk evaluation. FL allows collective, order-level model training whilst preserving privacy of data owners. A case study in the aerospace industry indicates that FL can be applied to predict buyers’ late payment risk with minimal performance loss.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:proeco:v:268:y:2024:i:c:s0925527323003274
    DOI: 10.1016/j.ijpe.2023.109095
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527323003274
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2023.109095?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sung, Hao-Chang & Ho, Shirley J., 2020. "Supply chain finance and impacts of consumers’ sustainability awareness," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    2. Xiaohan Huang & Jihong Sun & Xiaoyun Zhao & Baogui Xin, 2021. "Credit Risk Assessment of Supply Chain Financing with a Grey Correlation Model: An Empirical Study on China’s Home Appliance Industry," Complexity, Hindawi, vol. 2021, pages 1-12, June.
    3. Erik Hofmann & Oliver Belin, 2011. "Supply Chain Finance Solutions," SpringerBriefs in Business, Springer, number 978-3-642-17566-4, July.
    4. Dale S Rogers & Rudolf Leuschner & Thomas Y Choi, 2020. "Supply Chain Financing:Funding the Supply Chain and the Organization," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number q0245, February.
    5. Alexandra Brintrup & Johnson Pak & David Ratiney & Tim Pearce & Pascal Wichmann & Philip Woodall & Duncan McFarlane, 2020. "Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(11), pages 3330-3341, June.
    6. Ge Zheng & Lingxuan Kong & Alexandra Brintrup, 2023. "Federated machine learning for privacy preserving, collective supply chain risk prediction," International Journal of Production Research, Taylor & Francis Journals, vol. 61(23), pages 8115-8132, December.
    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. Yan, Nina & Sun, Baowen & Zhang, Hui & Liu, Chongqing, 2016. "A partial credit guarantee contract in a capital-constrained supply chain: Financing equilibrium and coordinating strategy," International Journal of Production Economics, Elsevier, vol. 173(C), pages 122-133.
    9. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    10. 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).
    11. 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.
    12. Arvind Ashta, 2021. "Falling like ninepins: How Greensill Capital’s supply chain finance took down its partners," Entreprendre & innover, De Boeck Université, vol. 0(1), pages 94-102.
    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. 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.
    2. 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.
    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. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    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. Shi, Jinzhao & Guo, Ju'e & Du, Qiang & Lin, Feng & Lai, Kin Keung & Cheng, T.C.E., 2020. "Optimal financing mode selection for a capital-constrained retailer under an implicit bankruptcy cost," International Journal of Production Economics, Elsevier, vol. 228(C).
    8. 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.
    9. Chakuu, Sumeer & Masi, Donato & Godsell, Janet, 2019. "Exploring the relationship between mechanisms, actors and instruments in supply chain finance: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 216(C), pages 35-53.
    10. Jiao Wang & Lima Zhao & Arnd Huchzermeier, 2021. "Operations‐Finance Interface in Risk Management: Research Evolution and Opportunities," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 355-389, February.
    11. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    12. Kern, Markus & Rudolph, Bernd, 2001. "Comparative analysis of alternative credit risk models: An application on German middle market loan portfolios," CFS Working Paper Series 2001/03, Center for Financial Studies (CFS).
    13. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    14. 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).
    15. Christian Gouriéroux & Alain Monfort, 2017. "Composite Indirect Inference with Application," Working Papers 2017-07, Center for Research in Economics and Statistics.
    16. Uddin, Gazi Salah & Tang, Ou & Sahamkhadam, Maziar & Taghizadeh-Hesary, Farhad & Yahya, Muhammad & Cerin, Pontus & Rehme, Jakob, 2021. "Analysis of Forecasting Models in an Electricity Market under Volatility," ADBI Working Papers 1212, Asian Development Bank Institute.
    17. Yang, Honglin & Zhuo, Wenyan & Shao, Lusheng & Talluri, Srinivas, 2021. "Mean-variance analysis of wholesale price contracts with a capital-constrained retailer: Trade credit financing vs. bank credit financing," European Journal of Operational Research, Elsevier, vol. 294(2), pages 525-542.
    18. Liu, Heng & Yan, Qiang, 2024. "Revenue-sharing contract in a three-tier online supply chain under EB platform financing," Finance Research Letters, Elsevier, vol. 59(C).
    19. Kexue Liu & Jean Salvati & Mr. Renzo G Avesani & Mr. Alin T Mirestean, 2006. "Review and Implementation of Credit Risk Models of the Financial Sector Assessment Program (FSAP)," IMF Working Papers 2006/134, International Monetary Fund.
    20. Avinadav, Tal & Levy, Priel, 2023. "The effect of an uncertain commission rate on the decisions of a capital-constrained developer," Operations Research Perspectives, Elsevier, vol. 11(C).

    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:eee:proeco:v:268:y:2024:i:c:s0925527323003274. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.