Financial markets face growing threats from misinformation that can trigger billions in losses in minutes. Most existing approaches lack transparency in their decision-making and provide limited attribution to credible sources. We introduce FinVet, a novel multi-agent framework that integrates two Retrieval-Augmented Generation (RAG) pipelines with external fact-checking through a confidence-weighted voting mechanism. FinVet employs adaptive three-tier processing that dynamically adjusts verification strategies based on retrieval confidence, from direct metadata extraction to hybrid reasoning to full model-based analysis. Unlike existing methods, FinVet provides evidence-backed verdicts, source attribution, confidence scores, and explicit uncertainty flags when evidence is insufficient. Experimental evaluation on the FinFact dataset shows that FinVet achieves an F1 score of 0.85, which is a 10.4% improvement over the best individual pipeline (fact-check pipeline) and 37% improvement over standalone RAG approaches.

翻译：金融市场正面临日益增长的虚假信息威胁，这些信息可在数分钟内引发数十亿美元损失。现有方法大多决策过程缺乏透明度，且对可信来源的溯源能力有限。本文提出FinVet——一种新颖的多代理框架，通过置信度加权投票机制，将两条检索增强生成（RAG）流程与外部事实核查相结合。FinVet采用自适应三层处理机制，根据检索置信度动态调整验证策略：从直接元数据提取到混合推理，再到完整的模型分析。与现有方法不同，FinVet提供基于证据的判定结论、来源溯源、置信度评分，并在证据不足时明确标注不确定性标志。在FinFact数据集上的实验评估表明，FinVet的F1分数达到0.85，相较于最佳独立流程（事实核查流程）提升10.4%，较独立RAG方法提升37%。