The integration of Artificial Intelligence (AI) into forensic auditing has emerged as a transformative approach to strengthening fraud detection and risk management within global financial institutions. Traditional auditing methods, while effective in retrospective analysis, often lack the speed and adaptability required to detect increasingly complex financial crimes in real time. AI-driven technologies, including machine learning, natural language processing, and predictive analytics, offer advanced capabilities for analyzing large volumes of transactional data, identifying hidden patterns, and uncovering anomalies that may indicate fraudulent activity. This review paper explores the evolving role of AI in forensic auditing, emphasizing its potential to enhance accuracy, efficiency, and timeliness in fraud detection processes. It further examines the practical implications for financial institutions, including improved compliance with regulatory frameworks, enhanced transparency, and proactive risk mitigation. Additionally, the review highlights challenges such as algorithmic bias, data privacy concerns, and the need for skilled professionals to interpret AI-generated insights. By synthesizing current research and industry practices, this paper provides a comprehensive assessment of how AI-enabled forensic auditing can redefine fraud detection and strengthen the resilience of financial systems in an increasingly digitized global economy.
Alaka et al. (Fri,) studied this question.