Emerging technologies and audit - part 2

In this lesson

1. 0:00 IASB feedback on data analytics in auditing
2. 1:06 Audit documentation with automated tools
3. 3:28 Identifying risks using data analytics
4. 5:01 Professional scepticism and automated outputs
5. 6:31 Machine learning and manual intervention
6. 7:00 Automated audit techniques as risk procedures
7. 8:05 Addressing overreliance on technology
8. 9:11 Implementing policies and training frameworks
9. 10:07 Managing automation bias in engagement teams
10. 11:26 Validating data accuracy and system completeness

About this lesson

You'll learn what the IAASB expects from auditors using these techniques, how to document your work properly, and how to avoid over-reliance on technology outputs. The lesson covers the IAASB's non-authoritative guidance on using automated tools for risk assessment and substantive procedures. You'll discover how data analytics can reveal patterns, anomalies and visualisations that manual testing might miss—such as unusual journal entries or seasonal revenue spikes—and how these insights can direct your audit work more effectively. Crucially, you'll understand that automation bias is a real risk, and that technology should enhance rather than replace your professional judgement. You'll also learn the practical requirements for audit documentation when using automated techniques. This includes recording the parameters you've used, the software selected, how data was captured, and whether matters are significant. The IAASB makes clear that documentation standards haven't changed—they've become more demanding. The lesson emphasises that managing overreliance on technology requires experienced team members, appropriate training, tailored software configurations, and explicit alerts when automation bias might influence your conclusions. This content is relevant to the AA module and reflects current IAASB standards and frequently asked questions issued between 2020 and 2021.

Duration: 12m