The finance team asks:
Why did recurring revenue move this month?
The database contains the answer.
But a raw ChatGPT-to-SQL connection does not contain the business definition.
It may not know:
- which subscriptions count
- how refunds are treated
- which timezone closes the month
- which legal entity is in scope
- whether the number is month-to-date or final
- whether a backfill changed yesterday's result
For finance reporting, the hard part is not generating SQL.
It is keeping the metric definition attached to the answer.
A production workflow should use:
- an approved finance view or semantic layer
- a dedicated read-only identity
- explicit period, entity, timezone, and currency
- a visible query plan before execution
- a result receipt with source, metric version, freshness, and filters
- an audit trail that survives follow-up questions
The follow-up question matters. When someone asks "which customers caused the change?", the workflow should preserve the original reporting period and metric version instead of silently rebuilding scope.
Natural language makes the interface easier.
It does not remove the need for accounting boundaries.
Full workflow: Connect ChatGPT to a SQL database for finance reporting
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