用证据支撑的审计风险预测模型，提前发现潜在违规
VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings
- 融合SFT与GRPO，基于统一证据标准进行审计推理
- 在未受罚企业中识别出高风险案例，表现超越所有基线
- 支持不确定时自动放弃判断，适合真实审计场景
在审计应用中，判断必须有合理证据支持。然而，标准金融语言模型更注重表达流畅性而非证据依据，其通用金融推理能力可能导致看似合理却模糊的答案，产生可信度缺口，难以用于审计工作。为此，我们提出VERA-8B，一种全新的端到端审计推理系统，可在监管处罚前识别审计风险。构建此类模型面临诸多挑战，因此前无机器学习工作专注于处罚前的审计预测。据我们所知，这是首个在同一证据标准下统一使用SFT与GRPO实现证据驱动审计推理的工作，性能优于所有评估基线。由于审计不容许无支持的断言，我们引入拒答与不确定性标注机制，对证据不全或不确定的情况进行延迟处理。最后，设计AuditBridge将原始财报文件转化为经验证的记录，并生成审核员可用的报告，实现金融与计算的高效衔接，具备广泛适用性。整体输出可审计、可审查，适用于实际审计流程。
原文摘要 · Abstract (English)
Across audit applications, judgments must be supported by reasonable evidence. However, standard financial language models prioritize fluency over evidence. They are built for general financial reasoning and may produce plausible but ambiguous answers, creating a grounding gap that makes them unsuitable for audit work. We address this gap with VERA-8B, a new end-to-end audit reasoning system that identifies audit risks before enforcement actions occur. Constructing such a model raises several challenges, as no prior machine learning work targets pre-enforcement audit prediction. To our knowledge, we are the first to unify SFT and GRPO for evidence-grounded audit reasoning under one evidence standard, achieving performance that surpasses all evaluated baselines. Because auditing cannot tolerate unsupported claims, we introduce abstention and uncertainty qualification to defer uncertain or evidence-incomplete cases. Finally, we design an AuditBridge to ground model reasoning for practical audit work. It transforms raw filings into verified records and then into reviewer-ready reports, bridging finance and computation with broad generality. Together, these components produce auditable, review-ready outputs suitable for practical audit work.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。