Data Analytics in Fraud Prevention and Detection by Government Internal Supervisory Apparatuses at Ministries/Institutions/Local Governments: A Mixed-Method Study

This study investigates differences in internal audit effectiveness and data analytics (DA) usage by the Government’s Internal Supervisory Apparatus (GISA) in fraud prevention and detection (FPD). It examines variations in DA usage based on GISA effectiveness and independence, motivations for DA use, application methods, and the effectiveness of DA tools. Using a mixed-method approach, data was collected via questionnaires and interviews. Independent Samples T-Test results indicate significant differences in internal audit effectiveness and DA usage between high and low DA usage groups across the full sample and within ministries/institutions. Significant differences in DA usage are also found based on GISA effectiveness and independence across the full sample and within ministries/institutions, but not within local governments. Key motivations for DA use include improving FPD efficiency, and DA has shown to enhance anomaly detection and audit scope, with Microsoft Excel and Audit Command Language (ACL) as the most used tools. Findings suggest optimized DA use through expanded access, training, and tailored resource.