Emerging Technologies in Auditing, Fraud Detection, and Internal Controls: A Review of AI, Data Analytics, Blockchain, and Continuous Monitoring Systems in U.S. Organizations

Abstract

Emerging technologies, including Artificial Intelligence (AI), data analytics, blockchain, robotic process automation (RPA), and continuous monitoring systems, are transforming auditing, fraud detection, and internal controls across U.S. organisations. This review synthesises literature on how these tools are reshaping audit practice and organisational governance. AI technologies such as machine learning and natural language processing improve audit quality by automating routine tasks, strengthening risk assessment, and enabling predictive auditing that moves beyond retrospective, sample-based testing. Data analytics supports full-population testing, ongoing anomaly detection, and proactive fraud prevention, improving the reliability of audit outcomes. Blockchain strengthens the integrity of audit evidence and reduces reconciliation burdens, while RPA reduces manual workload and frees auditors for higher-order analysis. Continuous monitoring systems provide real-time visibility into organisational processes, strengthening internal controls and regulatory compliance. Despite these benefits, adoption brings notable challenges: cybersecurity risks, data quality concerns, technological complexity, algorithmic bias, ethical uncertainty, and a shortage of specialised technical expertise. The review finds that, with proper governance, these technologies can offset such challenges while improving fraud detection, risk management, transparency, and accountability. Overall, the study concludes that emerging technologies are redefining the auditor's role and equipping organisations with tools to strengthen governance and maintain stakeholder confidence in an increasingly digital business environment.