用四个专业智能体协作生成更全面的金融报告。
FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios
- 设计四类专业智能体，分工完成文档分析、财务分析等任务。
- 在真实投资论坛数据上，报告接受率达62.00%，优于GPT-4o等模型。
- 适合需要深度金融分析的机构用户，提升报告质量与可信度。
金融报告生成涵盖宏观与微观经济分析，需大量数据处理。现有大模型多针对简单问答任务微调，难以全面分析真实金融场景。借鉴企业部门分工模式，我们提出FinTeam——一个由文档分析、分析、会计和顾问四类大模型智能体组成的协同系统。各智能体基于构建的专有数据集进行训练，具备特定金融能力。我们在真实在线投资论坛数据上构建了涵盖宏观经济、行业及公司分析的综合任务进行评估。人工评估显示，多智能体协作生成的报告接受率达62.00%，优于GPT-4o和Xuanyuan等基线模型。此外，系统在FinCUGE数据集上平均提升7.43%，在FinEval上准确率提高2.06%。项目代码已开源：https://github.com/FudanDISC/DISC-FinLLM/。
原文摘要 · Abstract (English)
Financial report generation tasks range from macro- to micro-economics analysis, also requiring extensive data analysis. Existing LLM models are usually fine-tuned on simple QA tasks and cannot comprehensively analyze real financial scenarios. Given the complexity, financial companies often distribute tasks among departments. Inspired by this, we propose FinTeam, a financial multi-agent collaborative system, with a workflow with four LLM agents: document analyzer, analyst, accountant, and consultant. We train these agents with specific financial expertise using constructed datasets. We evaluate FinTeam on comprehensive financial tasks constructed from real online investment forums, including macroeconomic, industry, and company analysis. The human evaluation shows that by combining agents, the financial reports generate from FinTeam achieved a 62.00% acceptance rate, outperforming baseline models like GPT-4o and Xuanyuan. Additionally, FinTeam's agents demonstrate a 7.43% average improvement on FinCUGE and a 2.06% accuracy boost on FinEval. Project is available at https://github.com/FudanDISC/DISC-FinLLM/.
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