Numbers You Can Defend: Using AI for Finance and Reporting
Seven tests, a 30-minute bake-off and a printable scorecard to tell an AI answer you can sign off from one you can only hope is right.
Free eBook: seven tests and a 30-minute bake-off to check whether an AI answer is repeatable, traceable and ready for finance sign-off.
Free eBook (PDF) · 14 pages · 18 min read · 569 KB · Published 27 October 2026
About this eBook
Anyone can now ask a business question in plain English and get a number back in seconds. The hard part is deciding whether you can put your name under it. This guide gives finance leaders and data teams a practical standard for sign-off: seven ten-minute tests for any AI tool, a 30-minute bake-off you can run on your own data, a printable scorecard, nine places where Indian finance data goes quietly wrong, and twelve questions to ask any vendor.
What you will learn
- Tell generating an answer from computing one, and ask the single question that separates them
- Run seven ten-minute tests: Repeat, Trace, Define, Scope, Reconcile, Doubt and Recover
- Set up a fair 30-minute bake-off on your own questions, with the expected answers written first
- Score any tool on a printable scorecard with a clear decision rule
- Spot nine traps in Indian finance data, from financial year to GST, credit notes and lakh versus crore
- Write ten definitions your whole team can rely on, and handle two numbers that disagree
- Ask twelve questions of any vendor about answers, definitions, people, data and exit
Key takeaway
An answer you can sign off can be repeated, traced, defined, scoped, reconciled, questioned and recovered.
Inside the eBook
- The sign-off problem: Why usually right is the wrong standard for money, and what an answer needs before you can defend it.
- Two ways a machine answers a question: Generating versus computing, a simple diagram, and the one question that separates them.
- The seven tests: Repeat, Trace, Define, Scope, Reconcile, Doubt and Recover, each with a ten-minute check and what pass and fail look like.
- The 30-minute bake-off: A fair way to compare two tools on your own data, with a printable scorecard and a decision rule.
- Nine places Indian finance data goes quietly wrong: Financial year, GST, credit notes, lakh and crore, several entities, date basis, rounding and currency.
- Getting your team ready: Ten definitions to settle, access that starts from nothing, a review rhythm and a four-step runbook.
- Twelve questions for any vendor: Questions on answers, definitions, people, data and exit, plus one honest page on where Trinetro fits.
Who it is for
- CFOs and finance controllers who sign off numbers
- Heads of data and analytics evaluating AI tools
- Founders who play both finance and data roles
- Teams in India working with Tally, GST and financial-year reporting
Questions
Who is this eBook for?
Finance leaders, data teams and founders who put their name under numbers and need to decide whether an AI tool can be trusted with them. No technical background is needed.
Do I need to use Trinetro to follow it?
No. The seven tests and the bake-off work on any tool. The last chapter explains where Trinetro fits and asks you to test us on your own data like any other.
How long does the bake-off take?
About 30 minutes. You write ten questions, note the expected answers from your ledger, run each twice and score the seven tests on the scorecard.
Is it specific to India?
The tests apply anywhere. One chapter covers Indian finance specifics such as the April to March financial year, GST, credit notes and lakh and crore.
Is the eBook free?
Yes. You can download the PDF straight from this page with no sign-up.
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