The Impact of Emerging Technology Stacks on Audit Quality and Risk: From Siloed Tools to Integrated Audit Ecosystems Across the Audit Cycle

This paper investigates how emerging technology stacks defined as the integrated, co-deployed combinations of blockchain, robotic process automation (RPA), deep learning, natural language processing (NLP), advanced data analytics (ADA), cloud audit platforms, and continuous auditing and monitoring (CAM) jointly reshape audit quality and the three components of the audit risk model: inherent risk, control risk, and detection risk. Drawing on a systematic review of 60 peer-reviewed publications, professional standards documents, regulatory reports, and industry studies spanning 2020 to 2026, the paper develops an original analytical framework, the Audit Stack Integration Model (ASIM), that characterises six configurations of audit technology stacks and their differential effects on audit quality across the full audit cycle from client acceptance to final reporting. Three analytical tables compare individual technology audit properties, stack configuration risk profiles, and empirical evidence of audit quality. The analysis demonstrates that integrated technology stacks produce emergent audit-quality effects that exceed the sum of individual technology contributions, but also introduce new systemic risks automation bias, integration failure, professional judgment displacement, and regulatory uncertainty that isolated technology analyses cannot identify.