The Use of Technology and Data Analytics in Modern Auditing: A Systematic Review

Abstract

The auditing profession is undergoing a significant transformation driven by advancements in technology and data analytics. While these innovations promise enhanced audit quality, efficiency, and reliability, their adoption and effectiveness vary across contexts. This systematic review synthesizes empirical evidence from 2020 to 2025 to examine the types, applications, outcomes, and challenges of technology and data analytics in modern auditing. Following PRISMA guidelines, a comprehensive search was conducted across Scopus, Web of Science, IEEE Xplore, and ScienceDirect, yielding 260 records. After screening, 10 studies met the inclusion criteria. Risk of bias was assessed using the ROBINS-I tool, and findings were synthesized narratively due to methodological heterogeneity. The review highlights the transformative impact of technologies such as big data analytics, artificial intelligence, and federated learning on audit quality and efficiency. Key findings include the positive role of client technological readiness in remote auditing, the moderating effect of cybersecurity on audit data analytics, and the challenges of skill gaps and resource constraints in small and medium-sized practices. However, benefits are context-dependent, with emerging markets facing unique regulatory and infrastructural barriers. While technology and data analytics offer substantial benefits for auditing, their successful implementation requires tailored strategies that address contextual, technical, and human factors. Future research should prioritize longitudinal and comparative studies to bridge the gap between experimental promise and practical application.