AI and Blockchain in Forensic Auditing: A Review of Tools for Investigating Financial Crimes in the U.S.

The increasing complexity of financial crimes in the United States has created a pressing need for more advanced, efficient, and accurate forensic auditing methods. Artificial Intelligence (AI) has emerged as a transformative tool in this area. AI helps detect, analyze, and prevent financial fraud. It automates data analysis, recognizes hidden patterns, and enhances decision-making in forensic audits. This study adopts a qualitative systematic literature review (SLR) approach. It examines the role of advanced technologies in forensic auditing and their application in investigating financial crimes in the United States. The paper draws from current literature, case studies, and industry reports. It examines major AI-powered tools, including machine learning algorithms, natural language processing (NLP), neural networks, and predictive analytics systems. These tools are deployed by U.S. auditing firms and regulatory bodies. The review highlights several benefits of these technologies. They improve the accuracy and timeliness of fraud detection. They help reduce audit fatigue. Additionally, they strengthen compliance with regulatory standards such as the Sarbanes–Oxley Act (SOX) and the Dodd-Frank Act. The paper concludes that advanced technologies cannot entirely replace human judgment in forensic auditing. Instead, it serves as a powerful complement that significantly enhances the ability to uncover and prevent financial crimes in the U.S. financial system.