BIG DATA ANALYTICS IN PUBLIC SECTOR AUDIT: TRANSFORMING RISK ASSESSMENT AND FRAUD DETECTION - Ferry Hendro Basuki; Rita J. D. Atarwaman; Yuyun Yuniarti Layn (CV. Adiba Aisha Amira, 2025)
The development of big data technology has brought about significant transformations in public sector audit practices, particularly in the areas of risk assessment and fraud detection. This study aims to comprehensively examine how big data analytics (BDA) is used in public sector audits through a literature review approach. By analyzing various previous studies, this article identifies the role of BDA in expanding the scope of audit analysis, improving the accuracy of risk assessments, and accelerating the fraud detection process through the use of intelligent algorithms and predictive analytics techniques. The study also highlights the challenges faced, including limited data quality, privacy issues, auditor skills, and the readiness of public institutions to adopt such technology. The results indicate that the application of BDA has the potential to improve the effectiveness and efficiency of public sector audits, while strengthening the accountability and transparency of state financial management. These findings provide academic contributions to modern auditing literature and offer practical implications for auditors and policymakers in formulating digital transformation strategies for public sector audits.