Data analytics and internal controls in U.S. financial systems: A review of fraud detection and financial reporting integrity

Abstract

This integrative review explores the role of data analytics and internal control systems in preventing fraud and financial reporting integrity in financial systems in the United States. The study adopts an integrative literature review approach that brings together a body of peer-reviewed research, regulatory publications, audit research, and financial technology literature to examine the use of analytics to detect fraud, the role of internal control over financial reporting, continuous monitoring, and emerging financial oversight technologies in an evolving regulatory and governance landscape. The results suggest that data analytics plays a crucial role in improving transaction surveillance, anomaly detection, risk assessment, and continuous auditing, especially when coupled with machine learning and AI monitoring tools. But the effectiveness of these technologies is still impacted by data quality challenges, model risk, implementation differences, explainability concerns and governance constraints. The review also continues to highlight the role of internal controls to ensure accountability, oversight and reliable reporting processes to support the effective utilization of analytical outputs in financial assurance environments. The study concludes that strengthening fraud detection and financial reporting integrity requires integrating analytics and internal controls within a unified governance framework supported by continuous monitoring, institutional accountability, and transparent oversight mechanisms.