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

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  • 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
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    References listed on IDEAS

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