How to check AI-generated
financial calculations.
Short answer: Check AI-generated financial calculations the way you would check a new colleague's work: calculate control totals before you ask, change one thing at a time, re-import the same file to catch double counting, and check signs, dates and period boundaries. The eight checks below take minutes and catch most errors in AI-built finance tools.
AI tools produce polished output quickly, and a polished chart is not proof the numbers are right. Finance people already have the right instincts: reconcile to a known figure, test one change at a time, and question anything that looks too clean. Here is how to apply them to AI-built tools.
1. Establish control totals before you ask
Calculate the key numbers independently in a spreadsheet: totals, one period on its own, and one category. Put them in the prompt. The tool is not done until it reproduces them.
2. Change one thing at a time
Ask for one feature, check it, then move on. When several changes arrive together, you cannot tell which one broke a total.
3. Re-import the same file
Import a small file with a known effect, then import it again. The second import must change nothing. This single test catches the most common import bug: double counting.
4. Check signs and direction
Confirm payments reduce net cash and receipts increase it, everywhere: cards, charts and tables. AI tools sometimes "fix" negative numbers.
5. Check dates and period boundaries
Put a transaction on the last day of a month and confirm it lands in the right period. Watch for MM/DD vs DD/MM confusion.
6. Ask the AI to flag, not guess
Include "flag ambiguous data instead of guessing" in every brief. An unknown category should show up as a question, not an invented label.
7. Keep the definition honest
A cash flow dashboard is not a profit and loss statement. State what the tool does not model: accruals, depreciation, unpaid invoices.
8. Break it on purpose
Feed it an empty file, a row with a missing amount, a duplicated row. Good tools show a clear message; bad ones silently produce wrong totals.
See these checks in context in how to build a cash flow dashboard with Claude Code, with sample data and expected totals.
Frequently asked questions
Can I trust numbers produced by AI?
Only after verification. Reconcile to independently calculated control totals and test edge cases before relying on the output.
What is the most common error in AI-built finance tools?
Two of the most common problems in AI-built dashboards are double counting on repeated imports and sign errors on payments.
Turn a CSV into a working cash flow dashboard.
A self-paced Claude Code workshop for bookkeepers, accountants and FP&A: six lessons, one project, sample data with expected totals. Taught by CFO Leo Godnik.
See the six lessons — $69 → Or start with the free lesson ↗