Definition
Audit data analytics (ADA) means using automated tools and techniques, such as scripts, spreadsheets or specialized software, to analyze large sets of entity data for audit purposes. Uses include risk assessment, testing complete populations of journal entries or invoices, finding duplicates or gaps, recalculating balances and spotting outliers.
Why it matters in an audit or review
ADA can test a whole population where sampling would test a small part of it, and it can target unusual items rather than random ones. It does not replace judgment: the auditor still has to define criteria, investigate each exception, and conclude. The data itself must be reliable, so the auditor tests completeness and accuracy of the extract and the source before relying on the output. Results and logic are kept in the working papers so a reviewer can reproduce them.
Example
The auditor imports the full year's AP disbursements (illustrative: 18,400 payments) and runs three tests: duplicate vendor, invoice number and amount; payments just under the approval limit; and payments to vendors created in the last 30 days. Fourteen items are flagged. The team traces each to support, finds two duplicates totaling $11,000, and proposes a correction.
Common mistakes
Running analytics on an extract the client gave without checking that it ties to the trial balance.
Sources
- AICPA, AU-C Section 240, Consideration of Fraud in a Financial Statement Audit (journal entry testing)
- AICPA, AU-C Section 500, Audit Evidence (reliability of information used)
- AICPA, AU-C Section 530, Audit Sampling
- AICPA, Guide: Audit Data Analytics (non-authoritative)
Definitions paraphrase AICPA and FASB guidance; read the standard itself before relying on a requirement. Example figures are illustrative. This page is guidance, not professional advice. Report an error