Building a Data-Driven Audit

In today's data-driven world, internal audit departments often spend a considerable amount of time manually filtering data, selecting samples, and testing each sample, particularly during planning and fieldwork. As technology rapidly advances and is increasingly adopted across the globe, it becomes more and more challenging to identify representative samples from large data populations. As a result, automating audit procedures and testing full populations is not only more feasible but also more efficient. In this course, designed uniquely for auditors and audit managers looking to keep pace with technological advancements, instructor Monica Royal shows you what it takes to streamline auditing processes, improve the quality of your work, and deliver more meaningful insights to your business.

Learning objectives
Differentiate between traditional and modern audit approaches, and articulate at least three advantages and disadvantages of using data analytics in the audit process.
Demonstrate understanding of the Global Internal Audit Standards related to technology by explaining how to establish, evaluate, and communicate about technological resources in the internal audit function.
Describe how data analytics can be incorporated into each phase of the audit process (planning, fieldwork, and reporting), providing at least one specific example for each phase.
Apply data analytics techniques to at least four different types of audits (financial, IT, fraud, and cybersecurity), demonstrating this ability through practical exercises and case studies.
Display proficiency in using data visualization techniques for audit reporting by creating a visual representation of audit findings for a financial audit scenario.