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English(EN) Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling

新AI框架通过不确定性建模增强审计风险评估

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了UMAR，一个新颖的多智能体框架，旨在通过明确建模不确定性和证据冲突来改进审计风险评估。UMAR利用三个专业智能体——MD&A文本智能体、财务比率智能体和CAM智能体——生成具有校准不确定性的独立风险评分。然后使用Dempster-Shafer理论聚合这些评分，该理论量化了智能体间的异议。在SEC 10-K filings的3,200个公司年度观测值上的评估表明，UMAR的性能优于基线模型，AUROC达到0.782，PR-AUC达到0.341，同时通过冲突模式提供了可操作的见解。 AI

影响 该框架可以通过利用多智能体系统和不确定性量化来提高财务风险评估的准确性和可解释性。

排序理由 该集群描述了一篇详细介绍新颖框架及其评估的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新AI框架通过不确定性建模增强审计风险评估

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该集群描述了一篇详细介绍新颖框架及其评估的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuhan Wang, Manqing Wang, Yixuan Lu, Zhaoyue Peng, Shengda Lin ·

    用于审计风险评估的多智能体框架，具有明确的不确定性和证据冲突建模

    arXiv:2606.15640v1 Announce Type: new Abstract: Audit risk assessment increasingly benefits from combining heterogeneous evidence sources, yet existing approaches typically produce point predictions without quantifying how well different evidence streams agree. We propose UMAR (U…

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