Fraud detection often includes analyzing large datasets of enterprise resource planning systems to locate irregularities. Analysis of the datasets often results in a large number of false positives, that is, entries wrongly identified as fraud. The aim of our research is to reduce the number of false positives by combining the red flag-based approach with process mining. The red flag approach presents hints for unusual behavior, whereas process mining reconstructs and visualizes the as-is business process from the underlying dataset. The combination of these two techniques allows for identification and subsequent visualization of possible fraudulent process instances with the corresponding red flags. We exemplarily applied our new approach to the purchase-to-pay business process to successfully identify 15 of 31 fraud cases in our dataset. Our false positive rate was 0.37%, which is considerably lower than rates reported in similar research papers.

欺诈检测通常包括分析企业资源计划系统的大型数据集以查找违规行为。数据集的分析通常会导致大量误报，即错误地将条目标识为欺诈。我们研究的目的是通过将基于红旗的方法与过程挖掘相结合来减少误报的数量。危险标记方法提供了异常行为的提示，而流程挖掘则从基础数据集中重建并可视化业务流程。这两种技术的组合允许对带有相应红色标记的可能欺诈流程实例进行标识和随后的可视化。我们将新方法示例性地应用于购买到付款业务流程，以成功识别数据集中31个欺诈案件中的15个。