Artificial Intelligence and the Transformation of Internal Auditing: Toward Augmented Auditing and AI Assurance – A Systematic Literature Review

Abstract

Artificial intelligence (AI) is transforming the internal audit function by fostering automation, continuous auditing, data analytics, and the emergence of new forms of assurance for intelligent systems. This study examines the current state of knowledge on this transformation through a systematic literature review conducted in accordance with PRISMA 2020. Searches conducted in Scopus and Google Scholar identified 58 publications; after duplicate removal and screening, 27 studies were retained and analyzed. The findings reveal a recent body of literature, with 19 of the 27 studies (70.4%) published between 2024 and 2026, characterized by a diversity of methodological approaches. The reviewed studies notably highlight the automation of audit activities, the development of continuous auditing, anomaly and fraud detection, and enhanced risk management. Compared with previous reviews, this study provides an integrated perspective linking AI technologies, emerging audit capabilities, augmented auditing, and AI Assurance. Its scope nevertheless remains limited by the use of only two literature sources and by the non-retrieval of 21 of the 48 full-text publications sought.

Classification JEL : M42, M49, O33

Paper type : Literature Review