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A machine learning-based study of credit risk in supply chain finance of listed service-oriented enterprises in China

Author

Listed:
  • Wang, Ziyang
  • Li, Yunpeng
  • Cui, Zhihao
  • Zheng, Weinan
  • Wang, Ting

Abstract

This study develops an early-warning system for financial distress among Chinese listed service enterprises by predicting ST and *ST designations. To address the underexplored role of supply chain dynamics in distress propagation, we construct a hybrid dataset combining conventional financial ratios with supplier/customer concentration, logistics efficiency, and partner stability indicators. A three-stage framework—feature selection using Mutual Information, SelectBest, and recursive feature elimination; evaluation of four resampling techniques; and benchmarking of eight machine-learning models—shows that XGBoost with SMOTE achieves the highest performance (F1 = 0.950). Supply-chain variables rank prominently among predictors and exhibit strong nonlinear threshold effects. The findings highlight the value of integrating supply-chain intelligence into credit-risk assessment and provide actionable guidance for regulators, investors, and managers.

Suggested Citation

  • Wang, Ziyang & Li, Yunpeng & Cui, Zhihao & Zheng, Weinan & Wang, Ting, 2026. "A machine learning-based study of credit risk in supply chain finance of listed service-oriented enterprises in China," Pacific-Basin Finance Journal, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:pacfin:v:96:y:2026:i:c:s0927538x25003804
    DOI: 10.1016/j.pacfin.2025.103043
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    Keywords

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    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • L80 - Industrial Organization - - Industry Studies: Services - - - General

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