Artificial Intelligence in Auditing: A Systematic Review of Audit Quality, Professional Judgment, and Governance Challenges

Abstract

In artificial intelligence (AI) was proving to be a transformative tool influencing the audit process and the quality of the resulting reports. As businesses and the digital landscape evolve, artificial intelligence (AI) was becoming a transformative technology that enhances audit quality, aids in professional judgment, and fortifies governance mechanisms. The challenges of big data, cloud computing, enterprise information systems, and digital financial transactions have rapidly grown, compelling traditional approaches to auditing to be obsolete and leading to the increased use of AI-integrated audit methods. This chapter provides a systematic review of the literature to synthesize current research on the application of AI in the field of auditing from the perspective of audit quality, professional judgment, and governance issues. A highly organized methodology for the review of the literature was used to find, categorize, and examine pertinent peer-reviewed scientific literature from well-traveled academic databases. All selected literature was systematically categorized based on the different kinds of AI technologies, the different kinds of audit applications, the audit governance dimensions, and emerging research themes to gain a comprehensive overview of the state of the art. The review highlights that the use of machine learning, deep learning, natural language processing, robotic process automation, explainable artificial intelligence, and generative artificial intelligence has greatly improved fraud detection, risk assessment, financial statement analysis, continuous auditing, compliance monitoring, and audit documentation. The synthesized evidence suggests that intelligent automation, predictive analytics, holistic data analysis, and evidence-based decision support are the areas in which AI was making an impact in enhancing audit quality, while also emphasizing the need for human expertise, professional skepticism, and ethical judgment in making audit decisions. The review also highlights important governance issues to tackle, such as transparency and explainability, algorithmic bias, cybersecurity, data privacy, auditor accountability, and regulatory compliance, to enable the responsible use of AI. Last but not least, the chapter outlines some of the most recent areas of research that are expected to help shape the future of intelligent auditing practices, such as explainable AI, generative AI, human-AI collaboration, AI governance frameworks, ethical auditing, and intelligent assurance systems, offering researchers, practitioners, regulators, and policymakers insightful ideas for crafting more transparent, reliable, and trustworthy AI-driven auditing approaches.