Audit and compliance functions have long occupied an uneasy space within organizations. They are essential for trust, accountability, and regulatory legitimacy, yet they are often perceived internally as slow, reactive, and detached from the realities of day-to-day operations. Traditionally, audits have relied on periodic sampling, manual reviews, and retrospective analysis, methods that made sense in a world of paper records and stable business processes. Today, however, organizations generate vast, continuous streams of transactional data, while regulatory expectations have expanded in scope and intensity. In this environment, legacy audit approaches struggle to keep pace. What is quietly reshaping this landscape is not a single technology, but the convergence of data pipelines and predictive modelling. Together, these tools allow audit and compliance teams to move from episodic oversight to continuous assurance, from rule-based checking to risk-sensitive prioritization, and from backward-looking reports to forward-looking insight. The promise is not merely efficiency, but a fundamental shift in how assurance functions contribute to organizational resilience. This article examines how data pipelines and predictive models are being applied to audit and compliance processes, what value they create, and what trade-offs they introduce. Rather than treating these technologies as technical add-ons, it frames them as socio-technical systems that reshape roles, incentives, and governance structures across the organization.
Adeola Yusuf (Thu,) studied this question.