Integrasi Big Data dalam Audit Forensik Sebagai Strategi Deteksi dan Investigasi Fraud Keuangan Daerah

Abstract

 This study aims to elaborate on the integration of big data in forensic audits as a strategy for detecting and investigating regional financial fraud. The increasing complexity and volume of financial data in the digital era creates new opportunities in detecting and investigating financial fraud. A more adaptive forensic audit approach to big data can improve the effectiveness of financial supervision and accountability, both in public and business entities. This study uses a qualitative approach with a literature study method to analyze the integration of big data technology into the forensic audit process. Data are analyzed through a thematic framework that identifies the role of big data analytics, such as predictive analysis and pattern recognition, in supporting the investigative audit process. The results of the study found that the use of big data can significantly accelerate the process of detecting transaction anomalies, expand the scope of the audit, and identify fraud patterns that were previously difficult to detect by conventional methods. This integration also increases the efficiency of the use of auditor resources in the investigation process. Thus, this study contributes to the development of a data-based forensic audit model, which combines digital analytical techniques in an investigative approach.