Reducing false positives in fraud detection: Combining the red flag approach with process mining

Abstract

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.