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.
Highlights
Many corporations worldwide use an enterprise resource planning (ERP) system to manage their business process, which continuously changes due to dynamic business requirements [1]
Standard business processes are usually incorporated into standard operating procedures (SOP), which are used as a reference to find any deviations
Evaluation design The evaluation in this research focuses on the following points: (1) finding the advantage of using the proposed fuzzy association rule learning method compared to using the association rule learning (ARL) method in the context of fraud detection, and (2) measuring the accuracy of both methods
Summary
Many corporations worldwide use an enterprise resource planning (ERP) system to manage their business process, which continuously changes due to dynamic business requirements [1]. Several methods of data mining, such as decision tree, neural network, bayesian network and support vector machine have been implemented in previous researches [10,11,12,13] to identify cases of fraud. These methods still have weaknesses in detecting fraud since they are not able to analyse the behaviour of control flow in the business process. The proposed method integrates process mining, fuzzy multi attribute decision making and fuzzy association rule learning to detect anomalies in a business process
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