Fraud Auditing in The Digital Age : Data Analytics-Based Fraud Prevention and Detection

Abstract

This study evaluates the fraud phenomenon in the digital era and the role of data analytics in fraud prevention and detection through a literature review. Digital transformation has created an increasingly complex fraud risk environment, where traditional audit methods are limited in capturing anomalies scattered across large volumes of data. Data analytics integrated with digital techniques such as machine learning, big data analytics, and forensic auditing offer new opportunities to strengthen the effectiveness of fraud audits. The reviewed literature demonstrates the ability of data-driven techniques to identify suspicious patterns, improve detection accuracy, and support preventive audit functions. While technology expands detection capacity, challenges such as limited auditor competency, data security issues, and the need for strong governance still require critical attention. This literature review approach integrates empirical and conceptual research findings from indexed scientific publications, providing a comprehensive overview of the development of data analytics in modern fraud audits. The research findings emphasize that the integration of technology and audit professionalism will be a key strategy for addressing fraud risks in the evolving digital era. The references used include recent academic studies from the past five years from various related disciplines.