  This paper introduces a Large Language Model (LLM)-based multi-agent
framework designed to enhance anomaly detection within financial market data,
tackling the longstanding challenge of manually verifying system-generated
anomaly alerts. The framework harnesses a collaborative network of AI agents,
each specialised in distinct functions including data conversion, expert
analysis via web research, institutional knowledge utilization or
cross-checking and report consolidation and management roles. By coordinating
these agents towards a common objective, the framework provides a comprehensive
and automated approach for validating and interpreting financial data
anomalies. I analyse the S&P 500 index to demonstrate the framework's
proficiency in enhancing the efficiency, accuracy and reduction of human
intervention in financial market monitoring. The integration of AI's autonomous
functionalities with established analytical methods not only underscores the
framework's effectiveness in anomaly detection but also signals its broader
applicability in supporting financial market monitoring.
