How do you use AI agents in finance without letting them decide anything?
Finance is the part of a business where a wrong answer is expensive and a missing check is invisible. That makes it a good place for an agent that reads everything and a bad place for one that decides anything.
In this guide
What can AI agents do in finance?
They can audit invoices against contracts, forecast the cash actually coming in, and review files, while deciding nothing themselves. On PROMIVO the Cash Runway Analyst forecasts the money that is actually coming, and names the money that is not. The Duplicate Payment Auditor finds the payments your exact-match control was never going to catch. The Invoice–Contract Reconciler catches the overcharges that match the purchase order perfectly.
Why should an agent decide nothing?
Because a payment, an approval or a filing belongs to a person with authority and a name. The finance agents are built to produce the list and the evidence, not the decision. The KYC File Reviewer is a good example: it finds what the onboarding file is missing, and decides nothing. Keep the same split in your own process.
Where do the useful findings come from?
From the places a simple control cannot see. A control that matches exactly will miss a payment that is nearly identical, and a check against the purchase order will pass an overcharge that matches the purchase order. The Expense Policy Auditor applies the expense policy nobody has read since it was written. The Month-End Close Agent reads the reconciliation, not the tick beside it.
How should you treat a forecast?
As a question, not an answer. Read what the forecast assumes and ask what happens if an assumption is wrong. A forecast that names the money that is not coming is more useful than one that is only confident, because it tells you which conversation to have with which customer.
What about the sensitivity of financial data?
Treat it as sensitive from the start. PROMIVO runs an agent on your own AI provider account, so start with an export you are comfortable sharing with that provider, and keep the real files out of it until you have seen what the agent does on a sample. Check your own policies on where financial data may go.
How should you start?
With one recurring task, such as the month-end reconciliation. Run the matching agent's free sample check on its page, with no card and no API key, to see what it produces on example data. Then run it on your own export with your own AI provider key, like every run on the platform, and have a person verify every item it flags before anything is acted on.