3 Use Cases for AI in Internal Audit

Key takeaways

• Full-population testing replaces the 1-of-25 sample once the repetitive work is automated, and every result traces back to its source.
• Three DataSnipper features, three real procedures: DocuMine, Document Matching, and Excel Agents each map to an internal audit and SOX test.
• Traceability is what makes it reviewable. Every AI answer links to its source document, so review and sign-off stay fast.
• The auditor keeps the judgment. The agent handles the manual work; the conclusion stays with the reviewer.

Where AI earns its place in auditing

The conversation about AI and auditing is everywhere. Running it on a live engagement is a different thing. 

Most auditors know AI is becoming part of the job. But there's still a big difference between using AI in theory and using it on a real audit procedure in a way that would hold up: clear audit trails, traceable evidence, and results that can be reperformed. 

We put that question to Armanino's risk advisory team. They walked through use cases from live client engagements. 

Below are all three, each an internal audit and SOX procedure, with DataSnipper-native features doing the work and Armanino's take on what changed. 

Use case 1: master data change validation

Vendor bank and payment method changes are a classic change control, and a routine internal audit and SOX test. Armanino's team validates them month over month. 

With DocuMine, they extract and compare the name, account number, and phone number on each change against the supporting documents, then confirm whether the details match. 

Running it across the full population instead of a 1-of-25 sample, they catch the digit a manual review misses and spend their time on the changes that look off. That's internal audit data analytics with the evidence attached to every row. 

The same approach covers user acceptance testing and employee, price, or item master data changes. 

"Doing it manually, you're going to miss a digit across dozens of bank account numbers. This let us test 100% of the changes." Claire Koch, Manager, Data Automation & Transformation at Armanino 

Use case 2: contract-to-invoice testing

Checking that invoice charges match contract terms is a common SOX and internal audit test, and a slow one when the terms are spread across long contracts. 

AI Extractions and DocuMine ingest and organize the contracts. Document Matching and Excel Agents then tie the extracted terms to the charges on each invoice and recalculate what's owed. 

Every data point is snipped to its source in the workbook, so the evidence stays traceable without flipping through stacks of PDFs. 

The same pattern extends to lease payments, sales commissions, rebates, royalties, and utility contracts. 

"No more flipping through 20 PDFs to find tick marks. It shifted our focus from repetitive work to the instances where things don't match." Ben Boxell, Senior Associate, Data Automation & Transformation at Armanino 

Use case 3: cash receipts reconciliation

Tying bank deposits back to the detailed reports behind them, across dozens of files, is the kind of SOX-related testing that eats hours. 

Document extraction pulls the data into Excel. An Excel Agent then matches deposits to report lines and works out which aggregated or timing-different payments reconcile to each deposit. 

Verification snips stay linked to the source, and the auditor reviews each suggested combination before it stands. 

Armanino applies the same approach to royalty collections and vendor rebate testing. 

"I estimate we saved about 25-plus hours on reconciliation." Amanda Finley, Manager, Data Automation & Transformation at Armanino 

What these internal audit tools share

Across all three, the pattern for AI and auditing is the same. The agent does the manual work and shows its source for every answer. The auditor keeps the judgment and the sign-off. 

That combination, full coverage plus traceable evidence, is what turns an AI pilot into a procedure a team can stand behind. It's the bar any AI audit software has to clear before it goes near a SOX control. 

Artificial intelligence audit work only counts if someone else can reperform it. 

Where to start with AI audit software

The teams closing the AI gap picked one use case, set the judgment line, and made the evidence traceable. Any internal audit or SOX function can start there. 

Pick the procedure that eats the most hours. Check whether the AI audit tools you already have can trace every answer back to a document. Then run it against the full population once and compare what it catches to what the sample caught. 

That test tells you more about data analytics for internal auditors than any vendor deck will.