Paper: arXiv 2403.19735

Authors: Taejin Park

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.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents an innovative multi-agent LLM framework for financial anomaly validation, emphasizing practical workflow integration over complex mathematical modeling. While the methodology introduces fresh ideas for reducing manual monitoring overhead on the S&P 500, the lack of rigorous quantitative backtesting and open-source code limits its empirical strength.

Research Flowchart

  flowchart TD
    A[Goal: Automate & Enhance Financial Anomaly Detection] --> B[Input: S&P 500 Index Data & System Alerts]
    B --> C[Multi-Agent Framework Initialization]
    C --> D[Data Conversion & LLM Web Research]
    D --> E[Institutional Knowledge Cross-Checking]
    E --> F[Report Consolidation & Verification]
    F --> G[Outcome: High-Accuracy Anomaly Validation & Reduced Human Intervention]

Cited References (6)

  1. Emergent autonomous scientific research capabilities of large language models (2023) — link
  2. Deep Learning for Anomaly Detection: A Survey (2019) — link
  3. More Agents Is All You Need (2024) — link
  4. Generative Agents: Interactive Simulacra of Human Behavior (2023) — link
  5. A Survey on Large Language Model based Autonomous Agents (2023) — link
  6. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (2023) — link

0 of 6 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.