Anomaly Detection in Inspection: A Hybrid Data Mining and MCDM Approach

trust growth in government and justice. Therefore, Simba inspection software data analysis, in order to detect and prevent inspectors' anomaly patterns, can insure the public trust and effectiveness of inspections. Based on a research gap identified in the research area, 1518 performance data were analyzed to detect anomalies. K-means clustering and classifications by decision tree, logistic regression, naïve bayes and support vector machine were employed to detect fraud, of which decision tree and logistic regression were better than others. Then the results synthesized with 243000 inspection report data analysis. In order to enhance practical side of research, data mining and decision science techniques were employed to find the fraudsters. As a result, collective anomaly detected and nine inspectors were identified as fraudsters. Lastly, IT-based solutions like software redesign and managerial tips were mentioned.