Artificial Intelligence in Accounting and Auditing : A Paradigm Shift Toward Smart Data and Continuous Auditing

Abstract

The accounting and auditing sectors are undergoing a radical transformation driven by artificial intelligence (AI) technologies. Professional practice has shifted from relying on traditional sampling to analyzing entire statistical populations. Auditing has shifted from periodic audits to continuous monitoring and real-time risk prediction. This in-depth article reviews contemporary applications of artificial intelligence in the fields of accounting and auditing, focusing on four key technologies : machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and deep learning (DL). Integrating these technologies has been shown to yield tangible improvements in audit efficiency and quality, including increasing fraud detection accuracy to 92% and reducing audit cycle time by 40–60% (Lin & Maginnis, 2025), while expanding data coverage to 100% of transactions from the traditional 5–10% sample size. The "Big Four" accounting firms (Deloitte, PwC, Ernst & Young, and KPMG) are leading adoption efforts, alongside major technology companies such as Microsoft, Amazon, and Google. These companies are developing specialized platforms (Kassar & Jizi, 2026) However, these technologies face fundamental challenges related to algorithmic bias, cybersecurity, the digital skills gap, professional accountability, and high costs. To ensure the responsible and effective adoption of these technologies, there is a need to develop integrated regulatory and training frameworks. This article presents a proposed model for the regulatory framework and practical recommendations for audit firms, regulators, and professionals.