The Future of External Audit: A Systematic Literature Review of Emerging Technologies and Their Impact on External Audit Practices

Abstract

Purpose: This study systematically reviews research on six emerging technologies in external auditing, Big Data, Blockchain, Machine Learning, Deep Learning, Artificial Intelligence (AI), and Robotic Process Automation (RPA), to clarify what is currently known and to identify where the main gaps remain. Rather than treating each technology in isolation, this study brings them together under a single integrative review to provide a consolidated reference point for scholars assessing their impact on external audit practices. Design/Methodology/Approach: Following a structured systematic review protocol, searches were conducted in Scopus, ScienceDirect and SpringerLink (2000–2024) using technology-related keywords combined with “audit”, “auditor” and “auditing”. After applying explicit inclusion and exclusion criteria, 471 records were reduced to 32 ABS-listed journal articles, which were analysed thematically. Findings: The review shows that research on emerging technologies in external auditing is still fragmented, with substantial variation in the depth and maturity of evidence across the six technologies. The strongest empirical base is concentrated in Big Data analytics and ML-based predictive models (including more advanced Deep Learning variants), whereas Blockchain and RPA work remains predominantly conceptual or confined to small-scale design-science implementations. Across technologies, most studies are single-country and either rely on auditors’ self-reported perceptions of adoption and impact or evaluate model performance without tracing effects on audit strategies and engagement outcomes, which limits external validity and construct measurement. Very few articles explicitly integrate the Audit Risk Model or other formal theories, and almost no work examines multi-technology “audit stacks” or generative AI, leaving substantial gaps in understanding how these tools jointly reshape inherent, control and detection risk across the audit cycle. Originality/Value: By integrating six technologies within a single external audit framework, the review offers a technology-specific evidence map and a targeted future research agenda that can guide scholars, audit firms and regulators in designing studies and policies aligned with actual gaps in the current literature.