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Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework

Taejin Park

TL;DR

The paper introduces an LLM-based multi-agent framework to automate validation and interpretation of financial data anomalies, addressing the need to reduce manual verification while maintaining rigor. It details a data-processing pipeline with an initial conversion agent, specialized expert agents (web research, institutional knowledge, cross-checking), and a consolidation/reporting agent, followed by a management discussion and human-in-the-loop. Demonstrations on the S&P 500 series (1980–2023) illustrate end-to-end anomaly detection, validation against known events (Black Monday, 2008 crisis, COVID-19), and correction of data inconsistencies through cross-checking. The work highlights the potential of AI-driven autonomous validation in financial monitoring, while emphasizing metadata governance and human oversight as essential components for reliable deployment.

Abstract

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.

Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework

TL;DR

The paper introduces an LLM-based multi-agent framework to automate validation and interpretation of financial data anomalies, addressing the need to reduce manual verification while maintaining rigor. It details a data-processing pipeline with an initial conversion agent, specialized expert agents (web research, institutional knowledge, cross-checking), and a consolidation/reporting agent, followed by a management discussion and human-in-the-loop. Demonstrations on the S&P 500 series (1980–2023) illustrate end-to-end anomaly detection, validation against known events (Black Monday, 2008 crisis, COVID-19), and correction of data inconsistencies through cross-checking. The work highlights the potential of AI-driven autonomous validation in financial monitoring, while emphasizing metadata governance and human oversight as essential components for reliable deployment.

Abstract

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.
Paper Structure (11 sections, 2 figures, 1 table)

This paper contains 11 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed Structure of Multi-Agent AI Framework for Anomaly Detection and Analysis
  3. Demonstration of the Multi-Agent AI Framework Using S&P 500 Series
  4. Outlier Detection
  5. Agent for Formulating Data Questions
  6. Data Expert 1: Web Research
  7. Data Expert 2: Institutional (Domain) Knowledge
  8. Data Expert 3: Cross-Checking
  9. Agent for Summary Reports
  10. Management Discussion
  11. Conclusion and Future Direction

Figures (2)

  • Figure 1: A multi-agent framework of validation process for data anomalies
  • Figure 2: Anomalies identified in the S&P500 series