总账数据中的异常检测：混合方法的结果
Anomaly Detection in General Ledger Data: Results from a Hybrid Approach
- German Research Center for Artificial Intelligence (DFKI) GmbH（德国人工智能研究中心（DFKI）有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出混合使用日记账分录测试与机器学习方法进行总账异常检测，以减少审计误报，提高检测性能和审计效率，实验基于合成数据验证。
AI中文摘要：
日记账分录测试（JETs）是年度审计中评估高风险审计领域和潜在重大错报的强制性部分。然而，由于JETs旨在基于领域知识检测已知模式，其结果列表通常非常庞大，需要审计师付出大量额外努力。为确保审计的经济效率，必须减少JET结果列表中的误报数量。特别是机器学习（ML）方法代表了改进该领域异常检测的一种有前景的方法。在这篇研究进展论文中，我们探讨了如何以混合方式将JETs与ML方法相结合的不同方法。我们提出了专门的模型，以提高异常检测结果的检测性能和有效性，从而提升审计效率。实验基于包含不同正常和异常日记账分录的合成数据。
英文摘要：
Journal Entry Tests (JETs) are a mandatory part of annual audits to evaluate and assess both highrisk audit areas and potential material misstatements. However, as JETs are designed to detect known patterns based on domain knowledge, the resulting lists are often very large and require substantial additional effort from the auditor. To ensure the economic efficiency of the audit, the number of false positives in JET result lists must be reduced. Especially machine learning (ML) methods represent a promising approach to improve anomaly detection in this field. In this research in progress paper, we investigate different approaches on how to combine JETs with ML-methods in a hybrid manner. We present specialized models to increase the detection performance and validity of anomaly detection results to improve audit efficiency. The experiments are based on synthetic data consisting of different normal and anomalous journal entries.
