用可解释AI提升银行异常交易检测，让审计员看得懂、信得过。
Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective
- 结合孤立森林与SHAP值，实现无监督异常检测与特征归因。
- 在合成数据集上达到0.91精确率和0.88召回率，优于三种基线。
- 轻量级仪表板让非技术审计人员也能理解模型决策依据。
银行业日益依赖自动化系统监控电子交易以识别欺诈行为，但传统基于规则的方法存在误报率高且无法提供输出解释的问题，限制了其在合规团队中的应用。本文提出一种面向内部审计流程的可解释人工智能（XAI）框架，用于银行交易异常检测。采用孤立森林（iForest）模型进行无监督异常评分，结合基于合作博弈论的SHAP（SHapley Additive exPlanations）层，生成交易级别的特征归因解释。通过轻量级Streamlit仪表板将结果可视化，便于无机器学习背景的审计人员使用。在合成银行数据集上的评估显示，该方法达到0.91的精确率和0.88的召回率，优于三种无监督基线模型。专家反馈表明，特征级解释显著提升了审计人员的信心与决策质量。该框架推动了可问责、透明的AI在受监管金融环境中的实际部署。
原文摘要 · Abstract (English)
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 teams. This paper introduces an Explainable Artificial Intelligence (XAI) framework tailored for banking transaction anomaly detection within internal audit workflows. An Isolation Forest (iForest) model performs unsupervised anomaly scoring, while a SHAP (SHapley Additive exPlanations) layer provides transaction-level, feature-attributed explanations grounded in cooperative game theory [8]. A lightweight Streamlit dashboard renders these outputs in a form accessible to audit professionals without machine learning expertise. Evaluation on a synthetic banking dataset yields 0.91 precision and 0.88 recall, outperforming three unsupervised baselines. Expert feedback confirms that feature-level explanations measurably improve auditor confidence and decision quality. The framework advances the practical deployment of accountable, transparent AI in regulated financial environments.
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