审计分析中的无监督异常检测：基于美国支出数据的案例研究
Unsupervised Outlier Detection in Audit Analytics: A Case Study Using USA Spending Data
AI总结：
本研究以美国卫生与公众服务部支出数据为案例，比较多种无监督异常检测算法，发现混合方法能提升审计中异常识别的稳健性与准确性。
摘要：
本研究探讨了无监督异常检测方法在审计分析中的有效性，以美国卫生与公众服务部（DHHS）的美国支出数据作为案例。我们采用并比较了多种异常检测算法，包括基于直方图的异常评分（HBOS）、稳健主成分分析（PCA）、最小协方差行列式（MCD）和K近邻（KNN），以识别联邦支出模式中的异常。该研究回应了在大型政府数据集中对高效且准确的异常检测日益增长的需求，而传统审计方法可能在此类场景中力不从心。我们的方法包括数据准备、算法实现以及使用精确率、召回率和F1分数进行性能评估。结果表明，结合多种检测策略的混合方法增强了复杂金融数据中异常识别的稳健性和准确性。本研究通过提供对各种异常检测模型相对有效性的见解，并展示无监督学习技术在提高审计质量和效率方面的潜力，为审计分析领域做出了贡献。研究结果对寻求在政府财务监督和风险管理中利用高级分析的审计师、政策制定者和研究人员具有启示意义。
英文摘要：
This study investigates the effectiveness of unsupervised outlier detection methods in audit analytics, utilizing USA spending data from the U.S. Department of Health and Human Services (DHHS) as a case example. We employ and compare multiple outlier detection algorithms, including Histogram-based Outlier Score (HBOS), Robust Principal Component Analysis (PCA), Minimum Covariance Determinant (MCD), and K-Nearest Neighbors (KNN) to identify anomalies in federal spending patterns. The research addresses the growing need for efficient and accurate anomaly detection in large-scale governmental datasets, where traditional auditing methods may fall short. Our methodology involves data preparation, algorithm implementation, and performance evaluation using precision, recall, and F1 scores. Results indicate that a hybrid approach, combining multiple detection strategies, enhances the robustness and accuracy of outlier identification in complex financial data. This study contributes to the field of audit analytics by providing insights into the comparative effectiveness of various outlier detection models and demonstrating the potential of unsupervised learning techniques in improving audit quality and efficiency. The findings have implications for auditors, policymakers, and researchers seeking to leverage advanced analytics in governmental financial oversight and risk management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
