The dangerous finance-close answer is not obviously wrong.
It is a polished explanation built from numbers that were never reconciled.
A ChatGPT database query can help investigate a variance. But month-end close is a controlled process, not an open-ended conversation with live tables.
Before the model explains anything:
- freeze the entity, ledger, period, currency, timezone, and cutoff
- bind words like revenue to an approved metric version
- reconcile subledgers and control accounts deterministically
- classify duplicate, late, reversed, and unposted entries
- return bounded exception groups instead of a giant export
- keep source, snapshot, filters, totals, and trace ID separate from the prose
If a reconciliation check fails, return an exception—not a narrative that rationalizes the difference.
The model may summarize the work. It should not become the system of record.
That distinction makes the workflow useful: analysts get a fast explanation, while reviewers can still reproduce the evidence without trusting the wording.
Full workflow: ChatGPT database query for finance close
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