Large Language Model-Augmented Financial Risk Intelligence: Integrating News Sentiment and Market Stress Indicators for Early-Warning Systems

Abstract

This paper proposes a conceptual and architectural framework for a large language model-augmented financial risk intelligence system that integrates real-time news sentiment analysis with market stress indicators to produce early-warning signals for systemic and idiosyncratic risk events. The increasing volume and velocity of textual data from financial news, social media, and regulatory filings, combined with the availability of high-frequency market microstructure data, create both opportunities and challenges for risk monitoring. Traditional econometric models often fail to capture nonlinear interactions between narrative-driven sentiment shifts and quantifiable stress measures such as volatility, credit spreads, and liquidity gaps. The framework described here leverages pre-trained large language models to extract semantic sentiment and thematic risk narratives, then fuses these signals with a set of stress indicators derived from multivariate market data. A discussion of the architectural trade-offs between centralized and federated deployment, the choice of transformer architectures for temporal reasoning, and the need for leakage-safe validation protocols is provided. Particular attention is given to the problem of benchmark contamination when evaluating early-warning performance, and to the design of economically credible tests that preserve causal ordering. The paper also examines governance requirements, including fairness in model outputs across different asset classes, robustness to adversarial news inputs, and the systemic risks arising from correlated model behavior across institutions. Policy implications for regulators and central banks are considered, and a research agenda for future work is outlined.