Integrating Artificial Intelligence in Audit Workflow: Opportunities, Architecture, and Challenges: A Systematic Review

This paper presents a systematic review of 100 peer-reviewed studies (2015-2025) on Artificial Intelligence (AI) applications in auditing, as they relate to machine learning (58%), natural language processing (31%), robotic process automation (24%), and other AI techniques (15%). Among other important results, it was shown that AI-powered anomaly detection is wiser than manual solutions by as much as 70 percent, and pilot projects experience improvements of up to 50 percent. The review breaks down AI methods by the various stages of auditing, such as planning, risk assessment, and reporting. This highlights the importance of machine learning in fraud detection and natural language processing in document analysis. Despite these improvements, challenges such as data quality, model explainability, and regulatory compliance persist. This paper proposes a reference architecture for AI-driven audit workflows and describes how data can be integrated, AI models can be developed, and a human in the loop can be provided. It highlights key research gaps, such as the absence of longitudinal studies on the impact of AI, comparisons of AI techniques, and the absence of regulatory frameworks. The review offers practical suggestions for integrating AI into auditing, which could be used to improve audit quality, increase coverage, and optimize resources in the digital audit space.