Data Analytics for Internal Audit Training Course

The Data Analytics for Internal Audit Training Course equips internal auditors, audit supervisors, and IT and financial auditors with the practical skills needed to use data throughout the audit lifecycle, from risk assessment and planning to fieldwork, reporting, and follow-up. Instead of drawing small samples and extrapolating, participants learn to test entire populations of transactions, isolate genuine exceptions, and give management and the audit committee evidence-based assurance on the risks that matter most. Government entities, ministries, banks, oil and gas companies, and large corporations now run their operations on enterprise resource planning, core banking, and payroll systems that record every transaction. Audit functions that still rely mainly on manual sampling cover only a fraction of that activity and can miss duplicate payments, fictitious vendors, control overrides, and unusual journal entries. Audit analytics closes this gap and frees auditors to focus their judgment on the exceptions that need investigation. The course covers computer-assisted audit techniques, data-driven risk assessment, and the design of analytic tests linked to specific risks and controls. Participants learn to request, extract, and validate data, confirm completeness and accuracy through record counts, control totals, and reconciliations, and apply stratification, aging, gap and duplicate detection, matching, Benford's Law, and outlier analysis. The program then applies these techniques to procure-to-pay, payroll, revenue, journal entries, user access and segregation of duties, and banking transactions, before moving to continuous auditing, continuous monitoring, and the governance of an analytics program. The course is delivered through hands-on exercises on realistic case datasets, analytic test design workshops, exception review sessions, and reporting practice using audit analytics and data tools. Participants leave with an analytics test library for common risks, a data request and validation checklist, an exception triage worksheet, and a roadmap for embedding analytics in their audit function.