Enhancing financial reporting accuracy and audit efficiency in U.S. organizations using integrated accounting and data analytics: A scoping review

Organizations in the United States face growing pressure to improve financial reporting accuracy and audit efficiency amid expanding data volumes, regulatory expectations, and digital transformation. Integrated accounting systems and data analytics, including artificial intelligence, full-population testing tools, and process mining, have emerged as potential responses, yet the literature remains fragmented across technological and organizational domains. This scoping review synthesizes evidence on how these technologies contribute to financial reporting accuracy and audit efficiency in U.S. organizational contexts. The review maps the evolution of relevant applications, examines associations with reporting-quality and efficiency outcomes, and identifies organizational enablers and challenges that condition those associations. Findings indicate a consistent movement away from sample-based assurance toward broader analytical coverage. Analytics and artificial intelligence are associated with improvements in reporting-quality attributes and with efficiency gains such as reduced close times and higher professional capacity. These benefits depend on human expertise, explainability, structured implementation, skills, and governance arrangements; technical, organizational, and regulatory barriers frequently co-occur with reported advantages. The review consolidates the evidence into the Integrated Accounting–Analytics Capability Framework, a layered conceptual architecture linking foundational data, technological capabilities, enactment mechanisms, conditioning factors, and outcomes. The framework advances understanding by integrating previously separate strands of research into a coherent explanatory structure and offers a foundation for cumulative scholarship and informed practice.