Get FP&A best practices, research reports, and more delivered to your inbox.
Last updated: July 2026.
Bottom line: You cannot evaluate AI accuracy from a demo, because demos run on data the vendor chose. The evaluation that works is a five-test protocol run on your own numbers during the proof of concept: backtest against months you already understand, trace every AI figure to source, re-ask for reproducibility, probe permission boundaries, and inspect the audit trail. A vendor that resists any of the five is answering your question.
Every FP&A vendor now claims accurate, explainable AI. The claims are not fake so much as untestable as marketed: accuracy on curated demo data says nothing about accuracy on your chart of accounts, your entity structure, and your messy actuals.
Finance teams already know how to solve this class of problem, because it is the same discipline as auditing any number: test it on known ground, trace it to source, and check who can see what. This page turns that instinct into a concrete protocol you can run inside a normal proof of concept, and it pairs with our FP&A software evaluation guide for the surrounding process. For the conceptual layer (what explainability means, why black-box scores fail finance), see explainable AI in FP&A; this page stays on testing.
Why AI accuracy claims are untestable as marketed
Three structural reasons. Demo data is chosen to behave, so the variance the AI "explains" was planted to be explainable. Accuracy is claimed at the model level, but your risk lives at the workflow level: a right number in the wrong entity context is a wrong answer. And marketing accuracy has no denominator: "highly accurate" against what baseline, measured how, on whose data? The fix is not better questions in the sales call; it is moving the test onto your data, where the ground truth is yours.
The five tests, at a glance
Run all five in the POC. The takeaway: each test has a concrete pass bar, and every one of them is cheap compared to trusting a demo.
Test 1: backtest accuracy on months you already closed
Give the AI three closed months, including one where something genuinely odd happened, and ask it to explain the variances. You already know the answers, which makes this the only accuracy test with ground truth. Grade on three things: did it find the drivers you know about, did it invent drivers you know are wrong, and did it say "insufficient data" where that was the honest answer. An AI that asserts confidently where it should hedge fails finance's bar regardless of how often it is right. Our guide to AI variance detection covers what good decomposition output looks like.
Test 2: lineage, or the click-through test
Take any figure the AI quotes and demand the path to source: which rows, which cells, which sync, from which system, as of when. Platforms built on grounded retrieval pass this in one click; platforms that generate numbers from model memory cannot, and the failure is disqualifying for anything your team will repeat to a board.
Test 3: reproducibility
Ask the same question twice in a session, then again tomorrow after actuals refresh. Identical answers, or a changed answer with a stated, versioned reason ("actuals for June updated on the 12th"), both pass. Silent drift fails: an analysis your team cannot re-derive is not an analysis, it is an anecdote.
Test 4: permission boundaries
Have a user scoped to one entity ask about consolidated results. The right behavior is a scoped answer or a clean refusal, never a leak. Then try harder: ask the AI to compare "your entity" against "the others." AI access must inherit the platform's permission model exactly, or the platform has built a side door around your controls.
Test 5: the audit trail
After a working session, pull the log. You are looking for a reviewable record of who asked what, when, and which data was touched, the same evidence standard you would want for any system a regulator or auditor might ask about. If the vendor's answer is "we can add logging," the feature does not exist yet.
Red flags in vendor answers
A short field guide from evaluations we have seen: "our model is highly accurate" with no denominator; a demo that cannot run on your uploaded trial balance; lineage that traces to a dashboard rather than to rows; "the AI only sees what it needs" without a demonstrable permission test; and any resistance to the backtest on grounds of setup effort, because the setup effort is the product.
How Aleph approaches accuracy and auditability
Stated plainly, because this page told you to demand it: Aleph's AI answers from your live, permission-scoped data, every figure traces to the underlying rows, analyses are re-derivable after refreshes with the change stated, and access is logged. We designed for the five tests because our customers' auditors run them. Bring your own closed months to a proof of concept and run the protocol; that is the demo we prefer to give.
Where standards are heading
If you want the standards anchor for an internal policy, the NIST AI Risk Management Framework is the reference most finance and risk teams are converging on: it frames trustworthy AI in terms of measurement, transparency, and accountability, which map cleanly onto the five tests above. Auditor expectations in this area are still forming; a team that can already produce lineage and access logs is ahead of the curve rather than chasing it.
Get the Claude Skills for finance ebook
The ebook's governance chapter extends this protocol into daily practice: which workflows to hand AI first, where the system-of-record line sits, and how packaged Skills keep AI output consistent and reviewable across a team. Download the Claude Skills for finance ebook.
Get FP&A best practices, research reports, and more delivered to your inbox.
