Natural Language Processing for Systemic Risk Monitoring in Financial Markets
Keywords:
natural language processing; systemic risk; financial stability; text as data; governance; early warning systemsAbstract
Systemic risk monitoring in financial markets has traditionally relied on quantitative indicators such as returns, volatilities, correlations, and balance-sheet ratios. Although these indicators remain essential, they often fail to capture the rapidly evolving narratives, expectations, and institutional disclosures that shape modern market dynamics. This paper examines natural language processing for systemic risk monitoring as a system-level engineering and governance challenge. It argues that textual analysis should not be treated as an isolated modeling technique but as part of a broader socio-technical infrastructure involving data acquisition, model architectures, human oversight, regulatory integration, and operational resilience. The discussion covers textual data sources, preprocessing pipelines, transformer-based architectures, semantic anomaly detection, early warning indicators, and explainability. It further analyzes structural trade-offs between model complexity and interpretability, latency and accuracy, centralized standardization and decentralized adaptation, and predictive sensitivity and stability. Case illustrations from corporate disclosures, supervisory documents, news flows, and social media demonstrate the potential and limitations of natural language processing in financial stability surveillance. The paper highlights fairness, accountability, sustainability, and robustness as first-order design constraints rather than post hoc adjustments. The conclusion outlines a research and policy agenda for integrating natural language processing into systemic risk monitoring in a manner that is transparent, adaptive, and institutionally legitimate.
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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.
