用专家指导的多智能体框架，从多年财报中发现隐蔽的财务造假证据链。
AuditAgent: Expert-Guided Multi-Agent Reasoning for Cross-Document Fraudulent Evidence Discovery
- 引入审计领域知识，结合风险先验与混合检索策略定位证据。
- 在真实监管文档数据集上，召回率和可解释性显著优于通用智能体。
- 适合金融监管、审计机构及合规科技开发者参考使用。
现实场景中的财务欺诈检测面临证据隐蔽且分散于复杂多年度财务披露的挑战。本文提出新型多智能体推理框架AuditAgent，融合审计领域专业知识，实现财务欺诈案例中细粒度证据链的精准定位。基于中国证监会发布的执法文件与财务报告构建专家标注数据集，方法集成主体级风险先验、混合检索策略与专用智能体模块，高效识别并聚合跨报告证据。大量实验表明，该方法在召回率与可解释性上均显著优于通用智能体范式，建立了自动化、透明化财务审计的新基准。结果凸显领域特定推理与数据构建对提升实际监管应用下稳健财务欺诈检测的重要性。
原文摘要 · Abstract (English)
Financial fraud detection in real-world scenarios presents significant challenges due to the subtlety and dispersion of evidence across complex, multi-year financial disclosures. In this work, we introduce a novel multi-agent reasoning framework AuditAgent, enhanced with auditing domain expertise, for fine-grained evidence chain localization in financial fraud cases. Leveraging an expert-annotated dataset constructed from enforcement documents and financial reports released by the China Securities Regulatory Commission, our approach integrates subject-level risk priors, a hybrid retrieval strategy, and specialized agent modules to efficiently identify and aggregate cross-report evidence. Extensive experiments demonstrate that our method substantially outperforms General-Purpose Agent paradigm in both recall and interpretability, establishing a new benchmark for automated, transparent financial forensics. Our results highlight the value of domain-specific reasoning and dataset construction for advancing robust financial fraud detection in practical, real-world regulatory applications.
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