Emerging technologies and audit - part 1

In this lesson

1. 0:00 Advanced Audit and Assurance overview
2. 0:08 Emerging technologies in audit practice
3. 0:51 Data analytics fundamentals explained
4. 2:46 Data analytics software and automation
5. 3:06 Real-world example: Netflix big data
6. 4:18 Audit data analytics definition
7. 5:01 How data analytics improves audit scope
8. 7:05 Data analytics audit procedures
9. 10:03 Advantages of data analytics auditing
10. 11:23 Challenges and limitations to consider
11. 12:25 Documentation and data storage requirements

About this lesson

This video explores the practical applications of data analytics in auditing—from testing entire transaction populations rather than samples to identifying anomalies and internal control weaknesses—and examines both the significant advantages and emerging challenges auditors face when implementing these technologies. Data analytics allows auditors to examine patterns, trends and deviations across complete datasets rather than relying on sampling alone. You'll discover real-world audit techniques such as comparing inventory purchase and sale prices to test net realisable value, visualising revenue by product and region to spot issues, and matching purchase orders to invoices and payments through system interrogation. The video also shows how data analytics can help detect segregation of duties problems and support sensitivity analysis on forecasts and assumptions. However, emerging technologies bring challenges worth understanding. These include inconsistent application across audit firms, lack of specific regulatory guidance, data integrity and privacy concerns, and the skills gap requiring auditor training. There's also the practical matter of documentation—you'll need to record how data analytics tests were performed so colleagues can repeat them—plus potential storage requirements and the risk of widening the expectations gap if clients assume you're testing absolutely everything when judgement still plays a role.

Duration: 14m