AI-Driven Analysis of Climate-Related Financial Risks in Corporate Annual Reports
Keywords:
climate risk disclosure, natural language processing, corporate annual reports, financial risk management, explainable AI, sustainability governanceAbstract
Climate-related financial risks have become a central concern for corporations, investors, and regulators, yet the information required to assess these risks remains embedded in unstructured narrative disclosures within corporate annual reports. This paper develops a system-level examination of AI-driven approaches for analyzing climate-related financial risk disclosures, with particular attention to the structural, governance, and infrastructural conditions that shape effective deployment. The discussion integrates regulatory disclosure frameworks, corpus construction practices, natural language processing architectures, and financial risk management requirements. It considers how domain-specific language models and semantic anomaly detection can support the identification of material climate risk language, while also highlighting the trade-offs among specificity, interpretability, fairness, robustness, and computational sustainability. The paper argues that AI systems for climate risk disclosure analysis should not be treated as isolated text analysis tools, but as components of broader socio-technical infrastructures that include data governance, auditability, regulatory alignment, and institutional accountability. It further examines the implications of model opacity, distributional shift, and carbon-intensive computation. The analysis draws on current disclosure initiatives, empirical research on climate-related textual analysis, and developments in explainable AI to propose a governance-oriented research agenda. The paper concludes that long-term value from AI-driven climate risk analysis depends less on algorithmic sophistication alone and more on the integration of AI outputs into transparent, contestable, and sustainable institutional processes.
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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.
