Hybrid association rule learning and process mining for fraud detection

Data mining and process mining provide solutions for fraud detection. The automated methods based on the historical data, however, still need an improvement. In this regard, we propose a hybrid method between association rule learning and process mining. The process mining, in this case, inspects the event log. Through an expert verification, the itemset of the association rule learning is used to generate positive and negative rules applied for compliance checking towards the testing dataset. The result then shows that the hybrid method has less false discovery rate and provides higher accuracy compared to that of the process-mining method in which the optimum accuracy lies in certain threshold of confidence level.