VERAFI：通过神经符号策略生成实现验证的代理金融智能
VERAFI: Verified Agentic Financial Intelligence through Neurosymbolic Policy Generation
AI总结：
VERAFI通过神经符号策略生成实现验证的代理金融智能，显著提升金融领域事实正确性与合规性。
AI中文摘要：
金融AI系统存在一个关键盲点：虽然检索增强生成（RAG）在查找相关文档方面表现出色，但在推理过程中，语言模型仍会在生成计算错误和违反监管规定时出现问题，即使检索完美。本文介绍了VERAFI（验证的代理金融智能），一种具有神经符号策略生成的代理框架，用于实现验证的金融智能。VERAFI结合了最先进的密集检索和交叉编码器重排序，与金融工具启用的代理和自动推理策略相结合，涵盖GAAP合规性、SEC要求和数学验证。我们在FinanceBench上的全面评估显示了显著的改进：传统密集检索与重排序仅达到52.4%的事实正确性，而VERAFI的综合方法达到94.7%，提高了81%。神经符号策略层本身比纯代理处理高出4.3个百分点，特别针对持续的数学和逻辑错误。通过将金融领域专业知识直接整合到推理过程中，VERAFI提供了一条通往满足监管合规、投资决策和风险管理严格准确性需求的可信金融AI的实用路径。
英文摘要：
Financial AI systems suffer from a critical blind spot: while Retrieval-Augmented Generation (RAG) excels at finding relevant documents, language models still generate calculation errors and regulatory violations during reasoning, even with perfect retrieval. This paper introduces VERAFI (Verified Agentic Financial Intelligence), an agentic framework with neurosymbolic policy generation for verified financial intelligence. VERAFI combines state-of-the-art dense retrieval and cross-encoder reranking with financial tool-enabled agents and automated reasoning policies covering GAAP compliance, SEC requirements, and mathematical validation. Our comprehensive evaluation on FinanceBench demonstrates remarkable improvements: while traditional dense retrieval with reranking achieves only 52.4\% factual correctness, VERAFI's integrated approach reaches 94.7\%, an 81\% relative improvement. The neurosymbolic policy layer alone contributes a 4.3 percentage point gain over pure agentic processing, specifically targeting persistent mathematical and logical errors. By integrating financial domain expertise directly into the reasoning process, VERAFI offers a practical pathway toward trustworthy financial AI that meets the stringent accuracy demands of regulatory compliance, investment decisions, and risk management.
