AI Finance Prompt Auditor
Paste the prompt you give ChatGPT or Claude for financial analysis. The auditor flags the missing context, undefined metrics, and open-ended framing that produce confident, wrong answers, then rewrites it.
Why does ChatGPT get financial calculations wrong?
Because a language model predicts plausible text rather than executing arithmetic, and because your prompt almost never contains the context that makes a financial answer correct for your company. The model does not know that your fiscal year starts in April, that customer success sits in COGS, or that Q2 is not comparable because of a bonus accrual. It fills those gaps with the most statistically common assumption and presents the result with complete confidence. This auditor flags the specific gaps in your prompt that produce that failure, then rewrites it.
How to use this tool
- 1
Paste your prompt
The actual prompt you use for financial analysis in ChatGPT, Claude, or any other assistant.
- 2
Review the risk flags
Eight checks run against it, each explaining what could go wrong and why it matters for finance work.
- 3
Take the rewrite
Get a restructured prompt with the missing context slots made explicit, ready to fill in and use.
A plausible answer and a correct answer look identical
In most domains a wrong AI answer is obviously wrong. In finance it arrives formatted, confident, and internally consistent. Five failure modes to guard against.
Arithmetic performed as text
A language model generates numbers token by token rather than computing them. It can produce a total that does not equal the sum of the rows above it, and nothing in the output indicates that anything went wrong.
Undefined metrics
Ask for net revenue retention without defining it and the model picks a definition. It may not be yours. The formula will be defensible in general and wrong for your board pack.
Silent assumptions
Missing months, unclear fiscal calendars, and ambiguous currency all get resolved silently. The model does not flag the assumption; it makes one and proceeds, and the assumption is invisible in the answer.
No traceability
The output gives you a number without the query, the rows, or the formula behind it. Verifying it means rebuilding the analysis yourself, at which point the tool has saved you nothing.
Confident presentation
The failure mode that matters most. Output arrives well-formatted and unhedged, which makes a wrong number more likely to reach a board undetected than a hand-built one would.
A better prompt narrows the gap. It does not close it.
Careful prompting genuinely reduces the error rate, and everything this auditor suggests is worth doing. But the underlying problem is architectural: a model asked to produce numbers directly will sometimes produce wrong ones, and no prompt eliminates that. The fix is to stop letting the model touch the arithmetic: have it plan the analysis, then execute the calculation in a sandboxed environment and return the formula alongside the result, so every number can be checked. That is how DataWyse is built, and it is why the trust question has an engineering answer rather than a disclaimer.
See how DataWyse answers thisQuestions finance teams ask about this tool
Can ChatGPT do financial analysis reliably?
It is genuinely useful for structuring an approach, explaining a concept, drafting commentary, and reviewing your logic. It is unreliable for producing numbers you intend to act on, because it generates arithmetic as text rather than executing it and lacks the company-specific context that makes a financial answer correct.
What makes AI hallucinate in financial analysis?
Missing context and open-ended framing. When a prompt omits the metric definition, the fiscal calendar, or how to handle a gap in the data, the model resolves the ambiguity with the most statistically likely option rather than asking. The result is coherent, confident, and specific to a company that is not yours.
Is it safe to paste financial data into ChatGPT?
Check your organisation's policy and the provider's data retention terms first. Consumer tiers have historically differed from enterprise agreements on whether inputs may be used for training. Many finance teams restrict this to aggregated or anonymised figures. This auditor runs entirely in your browser and nothing you paste leaves your machine.
What should a good finance prompt include?
The exact metric definitions you use, your fiscal calendar, the period and comparison basis, explicit instructions on how to handle missing or ambiguous data, a requirement to state assumptions separately from conclusions, and a requirement to show the formula behind every number so you can verify it.
Does better prompting fix the accuracy problem?
It reduces it substantially but does not solve it. As long as the model is generating numbers directly, some proportion will be wrong, and they will not look wrong. Solving it properly requires the calculation to happen outside the model, in code that can be inspected, with the result traceable back to source data.
Is this tool really free?
Yes. No signup, no email required, no usage limit. It runs entirely in your browser: nothing you type is uploaded to a server or stored anywhere. We build these because the people who find them useful are the people who eventually need a financial analyst that works the same way.
DataWyse answers the next thirty
Variance deep-dives, cash re-forecasts, scenario plans, board prep: asked in plain English, answered in minutes, with every number traceable to its formula and source data.