AI 中文总结
针对银行交易异常检测传统方法的局限，本文提出可解释人工智能框架，用隔离森林模型评分，SHAP层提供解释，通过Streamlit仪表板呈现结果，经实验评估性能良好，能提升审计人员信心与决策质量，推动AI在金融环境的实际部署。
AI 中文摘要
银行业越来越依赖自动化系统监测电子交易中的欺诈迹象，但传统基于规则的方法误报率高且无法为输出提供依据，限制了合规团队的效用。本文介绍了一种专为内部审计工作流程中的银行交易异常检测量身定制的可解释人工智能（XAI）框架。隔离森林（iForest）模型进行无监督异常评分，而SHAP（SHapley Additive exPlanations）层基于合作博弈论提供交易级、特征归因的解释。一个轻量级的Streamlit仪表板以审计专业人员无需机器学习专业知识就能访问的形式呈现这些输出。在合成银行数据集上的评估产生了0.91的精度和0.88的召回率，优于三个无监督基线。专家反馈证实，特征级解释显著提高了审计人员的信心和决策质量。该框架推动了在受监管金融环境中可问责、透明的人工智能的实际部署。
英文摘要
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.
Comments9 pages, 2 tables. Author's preprint. A revised version of this work has been published in the International Journal of Engineering Development and Research (IJEDR)
Journal refInternational Journal of Engineering Development and Research (IJEDR), Vol. 14, No. 2, May 2026
