Keywords
Explainable early warning model
Decision-making path
Risk influencing factors
Submitted : 2025-12-10
Accepted : 2025-12-25
Published : 2026-01-09
Abstract
With the development of the times, the market-oriented reform of the capital market has been continuously deepened. As a key channel for direct corporate financing, the scale of the corporate bond market continues to expand. However, the frequent occurrence of default events has also significantly impacted market stability and investor interests. Traditional bond default risk early warning models often focus on improving prediction accuracy but generally suffer from the “black box” problem, making it difficult to clearly explain the formation logic of risk warning results. This greatly restricts the application value of the models in practical decision-making. In view of this, this paper analyzes the explainable early warning model and decision-making path for corporate bond default risk and proposes relevant strategies.
References
Černevičienė J, Kabašinskas A, 2024, Explainable Artificial Intelligence in Finance: A Systematic Literature Review. Artificial Intelligence Review, 57(8): 1–45.
Davis R, Lo A, Mishra P, et al., 2023, Explainable Machine Learning Models of Consumer Credit Risk. Journal of Financial Data Science, 5(4): 9–39.
White L, 2010, Markets: The Credit Rating Agencies. Journal of Economic Perspectives, 24(2): 211–226.
Altman E, Hotchkiss E, 2006, Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt (3rd ed.), John Wiley & Sons.
Ohlson J, 1980, Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1): 109–131.
Lu J, Zhuo Y, 2021, Modelling of Chinese Corporate Bond Default: A Machine Learning Approach. Accounting & Finance, 61(5): 6147–6191.
Altman E, 1968, Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4): 589–609.
Ashbaugh-Skaife H, Collins D, LaFond R, 2006, The Effects of Corporate Governance on Firms’ Credit Ratings. Journal of Accounting and Economics, 42(1–2): 203–243.
Rudin C, 2019, Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence, 2019(1): 206–215.
Duffie D, Saita L, Wang K, 2007, Multi-Period Corporate Default Prediction with Stochastic Covariates. Journal of Financial Economics, 83(3): 635–665.
Guyon I, Elisseeff A, 2003, An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 2003(3): 1157–1182.
Ke G, Meng Q, Finley T, et al., 2017, LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017).
Ribeiro M, Singh S, Guestrin C, 2016, “Why Should I Trust You?” Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘16), 1135–1144.
Gama J, Žliobaitė I, Bifet A, et al., 2014, A Survey on Concept Drift Adaptation. ACM Computing Surveys, 46(4): 1–37.
Lundberg S, Lee S, 2017, A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30 (NeurIPS 2017), 4765–4774.