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Audit Data Analytics

This episode of the ISACA Certified Information Systems Auditor (CISA) exam prep series explores how data analytics and technology tools are transforming IS audit practice. It covers the use of computer-assisted audit techniques, the distinction between continuous auditing and continuous monitoring, five automated real-time techniques, and the growing role of artificial intelligence in the audit life cycle.

What this episode covers

Watch the full episode above for the worked examples and detailed explanations of each concept.

Frequently Asked Questions

How does audit data analytics differ from traditional testing?

Analytics allows the auditor to test full data sets rather than samples, hunting for abnormalities, variances, and patterns across the entire population at once. This makes it possible to catch the anomaly hiding in the crowd that sampling would miss, and it can also be used during planning to assess risk across large transaction volumes.

What is the difference between continuous auditing and continuous monitoring?

Continuous auditing is performed by the auditor and reports on the subject in near real time with a short gap between event and reporting. Continuous monitoring is performed by the organisation itself to watch the performance of its own processes and systems — for example, real-time antivirus. Monitoring lacks the independence that audit provides and can never replace the audit function.

What are the five automated techniques that power continuous auditing?

The five techniques are: the audit review file with embedded modules (software embedded in the application to monitor transactions selectively), snapshots (pictures of a transaction path through processing), audit hooks (flags embedded to trigger early alerts), the integrated test facility (dummy entities and test transactions run alongside live ones), and continuous and intermittent simulation (the system simulates the application and audits transactions meeting defined criteria).

How is artificial intelligence reshaping audit work, and what risks must auditors watch for?

AI increases efficiency by automating tedious tasks such as document classification, text summarisation, and sentiment analysis, and it can reduce audit risk by enabling larger samples and deeper analysis across the audit life cycle. However, a person must always interpret results in the end, training data must be correct and cover unusual cases, and tools built by humans inherit human bias.

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Reference: This article is based on concepts discussed in Audit Data Analytics.