Explainable AI for Early Warning of Corporate Financial Distress Using Unstructured Disclosure Data
Keywords:
explainable artificial intelligence; corporate financial distress; unstructured disclosures; early warning systems; natural language processing; model governance; financial riskAbstract
Corporate financial distress prediction has traditionally relied on structured accounting ratios and market indicators, but such indicators often exhibit substantial lag and provide limited insight into the underlying managerial narrative. This paper presents a system-level analysis of explainable artificial intelligence for early warning of corporate financial distress using unstructured disclosure data. The paper argues that textual disclosures, particularly risk factor narratives and management discussion, contain early signals that complement quantitative financial information. It develops an architectural perspective on how natural language processing models, gradient boosting classifiers, and semantic anomaly detection can be integrated into a governed early warning infrastructure. The analysis examines structural trade-offs between post hoc explanation methods and inherently interpretable models, and discusses the implications of choosing centralized versus federated data processing pipelines. The paper further addresses temporal validation, distributional shift, fairness across sectors and firm sizes, and the operational requirements of regulatory reporting and internal audit. Explainability in this context is presented not only as a technical property of a model but also as an institutional capability that combines documentation, human review, confidence calibration, and accountability mechanisms. The paper concludes with forward-looking policy considerations for sustainable deployment in financial institutions, supervisory authorities, and credit market infrastructures. The contribution is intended to guide system architects, risk officers, and regulators who must balance early warning effectiveness with transparency, robustness, and fairness.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
