Leveraging Large Language Models for Anomaly Event Early Warning in Financial Systems

Given the strong semantic concealment, complex data structures, and high responsiveness requirements of anomaly events in financial systems, this study investigates advanced anomaly early warning methods that integrate large language models (LLMs) to enhance detection and response capabilities. The research systematically outlines semantic processing approaches for multi-source heterogeneous data, including structured transaction records, unstructured textual reports, and real-time market indicators, to ensure comprehensive analysis of potential anomalies. It further details the construction path for risk identification models, emphasizing feature extraction, multi-dimensional correlation analysis, and adaptive learning mechanisms that allow the system to dynamically adjust to evolving financial patterns. In addition, the study presents the generation and push mechanism for early warning information, ensuring that alerts are delivered promptly to relevant stakeholders while maintaining high interpretability and actionable insight. The system's response efficiency and recognition performance are rigorously evaluated in real trading environments, demonstrating the framework's robustness under high-frequency and high-volume transaction conditions. Comparative experiments with various baseline models show that the proposed approach exhibits strong adaptability and practical value, achieving superior performance in both recognition accuracy and response timeliness. These results indicate the potential of LLM-enhanced anomaly early warning systems to support more reliable and intelligent risk management in complex financial environments.