Research on Credit Risk Identification Method for Supply Chain Finance Based on Heterogeneous Graph and Dynamic Distillation

DOI

10.2991/978-94-6463-916-2_35How to use a DOI?

Keywords

Supply chain finance; Graph contrastive learning; Knowledge distillation; Minority class identification

Abstract

Accurately identifying default risk among small and medium-sized enterprises (SMEs) in supply chain finance is challenging due to complex inter-firm relationships, severe class imbalance, and the need for interpretability. We propose a lightweight credit risk assessment framework that combines structure-aware embeddings from a heterogeneous supply-chain graph using Graph Contrastive Learning (GCL) with a distilled student classifier based on Dynamic-Temperature Knowledge Distillation (DKD-MLP). Class rebalancing (SMOTETomek) and feature pruning (RFE) further enhance training stability. On real-world SME and ChiNext board data from the Shenzhen Stock Exchange (2018–2023), the model achieves an AUC of 0.912, PR-AUC of 0.558, F1-score of 0.625, and Recall of 0.592, outperforming baselines such as GCN and GraphMLP. Ablation confirms the value of GCL and DKD, while SHAP analysis highlights the default history of core firms and accounts-receivable turnover as dominant predictors. The framework improves minority-class identification while maintaining efficiency and transparency, making it suitable for practical financial decision-making.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yanzi Liu
PY  - 2025
DA  - 2025/12/22
TI  - Research on Credit Risk Identification Method for Supply Chain Finance Based on Heterogeneous Graph and Dynamic Distillation
BT  - Proceedings of the 2025 4th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2025)
PB  - Atlantis Press
SP  - 302
EP  - 310
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-916-2_35
DO  - 10.2991/978-94-6463-916-2_35
ID  - Liu2025
ER  -