Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionLanguage ModelingLanguage ModellingLarge Language ModelManagementSimilar Papers 제목 키워드 기반
Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy
Anomaly detection is a challenging task, particularly in systems with many variables. Anomalies are outliers that statistically differ from the analyzed data and can arise from rare events, malfunctions, or system misuse…
Anomaly DetectionA FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series
Financial markets are inherently volatile and prone to sudden disruptions such as market crashes, flash collapses, and liquidity crises. Accurate anomaly detection and early risk forecasting in financial time series are …
Anomaly DetectionDive into Time-Series Anomaly Detection: A Decade Review
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has …
Anomaly DetectionTime SeriesTime Series Anomaly DetectionDetecting Financial Market Manipulation with Statistical Physics Tools
We take inspiration from statistical physics to develop a novel conceptual framework for the analysis of financial markets. We model the order book dynamics as a motion of particles and define the momentum measure of the…
Anomaly DetectionAdvancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potential…
Anomaly DetectionSentence