FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis

2025-09-22

AI Summary

Addressing critical challenges in financial analysis—namely overconfidence, miscalibrated confidence estimates, and non-actionable insights—this paper proposes a multi-agent collaborative analytical framework. The framework comprises five specialized agents (financial, market, sentiment, valuation, and risk), integrated with retrieval-augmented generation (RAG) and domain-specific knowledge retrieval. A structured, safety-aware debate mechanism enables cross-dimensional evidence fusion and dynamic opinion evolution: agents rigorously challenge and refine each other’s reasoning while preserving logical consistency, thereby substantially mitigating overconfidence and improving confidence calibration and interpretability. Experiments demonstrate that the framework consistently generates high-quality, well-calibrated analytical reports across multiple time horizons and produces actionable investment strategies. It significantly outperforms baseline methods in both automated LLM-based evaluation and human expert assessment.

Technology Category

• Multiagent Systems: Mechanism Design
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

We introduce FinDebate, a multi-agent framework for financial analysis, integrating collaborative debate with domain-specific Retrieval-Augmented Generation (RAG). Five specialized agents, covering earnings, market, sentiment, valuation, and risk, run in parallel to synthesize evidence into multi-dimensional insights. To mitigate overconfidence and improve reliability, we introduce a safe debate protocol that enables agents to challenge and refine initial conclusions while preserving coherent recommendations. Experimental results, based on both LLM-based and human evaluations, demonstrate the framework's efficacy in producing high-quality analysis with calibrated confidence levels and actionable investment strategies across multiple time horizons.

Problem

• Integrating collaborative debate with domain-specific RAG for financial analysis
• Mitigating overconfidence through safe debate protocol for reliable recommendations
• Producing multi-dimensional insights with calibrated confidence across time horizons

Innovation

• Multi-agent framework integrating collaborative debate with RAG
• Five specialized agents synthesize multi-dimensional financial insights
• Safe debate protocol challenges conclusions while preserving recommendations