Anomaly detection in business processes using process mining and fuzzy association rule learning

TLDR

Enterprise resource planning systems generate massive process logs that are difficult to monitor manually, and process mining is used to analyze conformance between logs and standard procedures. The study aims to develop a method for detecting anomalies in these large ERP logs. The proposed approach integrates process mining with fuzzy multi‑attribute decision making to compute anomaly rates and fuzzy association rule learning to generate rules for anomaly detection. Experiments show the association rule learning method achieves 0.975 accuracy at 0.9 confidence, while fuzzy association rule learning reaches 0.925 accuracy at 0.3 confidence, demonstrating its ability to detect fraud even at low confidence levels.

Abstract

Abstract Much corporate organization nowadays implement enterprise resource planning (ERP) to manage their business processes. Because the processes run continuously, ERP produces a massive log of processes. Manual observation will have difficulty monitoring the enormous log, especially detecting anomalies. It needs the method that can detect anomalies in the large log. This paper proposes the integration of process mining, fuzzy multi-attribute decision making and fuzzy association rule learning to detect anomalies. Process mining analyses the conformance between recorded event logs and standard operating procedures. The fuzzy multi-attribute decision making is applied to determine the anomaly rates. Finally, the fuzzy association rule learning develops association rules that will be employed to detect anomalies. The results of our experiment showed that the accuracy of the association rule learning method was 0.975 with a minimum confidence level of 0.9 and that the accuracy of the fuzzy association rule learning method was 0.925 with a minimum confidence level of 0.3. Therefore, the fuzzy association rule learning method can detect fraud at low confidence levels.