In an age where financial ecosystems are more interconnected than ever, fraud and misrepresentation continue to evolve with the same sophistication as the systems meant to detect them. The emergence of artificial intelligence (AI) in forensic accounting has ushered in a paradigm shift, transforming the traditional methods of fraud detection into highly predictive, real-time, and intelligent mechanisms. This paper explores the evolving landscape of AI-powered forensic accounting through secondary data analysis—drawing from existing literature, case studies, financial reports, and regulatory findings. It examines the fusion of machine learning, natural language processing, and predictive analytics within forensic accounting and how these technologies enhance transparency, efficiency, and accuracy. The study highlights the implications for auditors, regulators, and financial institutions, while addressing ethical concerns and the need for accountability frameworks. Ultimately, the paper underscores how AI-driven forensic accounting represents not just a technological revolution but a structural realignment of financial truth-seeking in the digital era
Ann R. Thomas (2026) studied this question.