Big Data and Its Transformative Role in the Auditing Process: A Review of Current Applications and Future Directions

Abstract

This Systematic Literature Review (SLR) explores the transformative role of Big Data and Data Analytics (BDA) in the auditing process, synthesizing findings from 10 key studies published between 2018 and 2023. The research highlights the significant impact of Big Data on enhancing audit quality, improving risk assessment, fraud detection, and audit efficiency. Big Data enables auditors to move beyond traditional methods, offering comprehensive audits through the analysis of entire datasets rather than limited samples. The review also discusses the integration of machine learning, artificial intelligence (AI), and robotics as complementary technologies that increase the effectiveness and automation of audit tasks. Challenges such as data privacy, the need for specialized auditor skills, and the legitimacy of Big Data technologies in traditional audit practices are examined. The study identifies motivating factors for the adoption of Big Data in auditing, such as institutional pressures, regulatory requirements, and organizational readiness. Furthermore, future research directions focus on the integration of AI, blockchain, and continuous auditing technologies, with an emphasis on addressing ethical concerns, data security, and the evolution of auditors’ skill sets. This review provides valuable insights into how Big Data is reshaping the auditing profession and the future challenges and opportunities in the integration of these technologies.