As technology and data analytics become increasingly integrated into the audit process, auditors need to apply professional skepticism when evaluating information generated through automated tools and data-driven methods. This course helps participants strengthen their ability to assess audit evidence, recognize potential risks, and use professional judgment in an increasingly technology-driven audit environment.

Participants will explore the risks and opportunities associated with data-driven audits, including techniques for assessing the reliability of audit evidence obtained through data analytics and automated tools. The course also introduces divergent and convergent thinking as approaches for examining evidence from different perspectives, challenging assumptions, and identifying potential inconsistencies.

Through practical discussion and application, participants will consider how critical thinking and curiosity can strengthen professional skepticism when evaluating audit evidence. They will examine potential inconsistencies between data analytics results and responses received through governmental entity inquiries, considering how these differences may affect the audit and what additional questions or procedures may be appropriate.

The course provides practical approaches auditors can use to apply professional skepticism throughout a data-driven audit. Participants will leave with techniques for evaluating technology-generated evidence, challenging assumptions, and using critical thinking to arrive at appropriate audit conclusions.