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English(EN) Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective

可解释人工智能框架增强了审计人员对银行业务交易异常检测的能力

作者 PulseAugur 编辑部 · [2 个来源] ·

本文介绍了一个专为内部审计目的设计的、用于银行业务交易异常检测的可解释人工智能（XAI）框架。该系统利用隔离森林模型进行无监督异常评分，并利用SHAP层提供特征归因解释。Streamlit仪表板使没有机器学习专业知识的审计人员也能访问这些输出，评估结果显示与基线方法相比，精确率和召回率均有所提高，专家反馈表明审计人员的信心和决策质量得到了提升。 AI

影响 通过使人工智能输出对审计人员具有可解释性，增强了受监管金融环境的透明度和决策质量。

排序理由 该集群包含一篇研究论文，详细介绍了银行业务交易异常检测的新框架。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

可解释人工智能框架增强了审计人员对银行业务交易异常检测的能力

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该集群包含一篇研究论文，详细介绍了银行业务交易异常检测的新框架。
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完整方法见我们的编辑标准。

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Anupa Lodhi ·

    面向银行业务交易异常检测的可解释人工智能：内部审计视角

    arXiv:2607.13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their…

  2. arXiv cs.AI TIER_1 English(EN) · Anupa Lodhi ·

    面向银行业务交易异常检测的可解释人工智能：内部审计视角

    The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance te…

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