Finance ops · Production

Ramp Policy Agent automates expense review for 1,000+ finance teams

The problem

Manual expense review was inconsistent and risky — the same policy produced different outcomes depending on the reviewer, out-of-policy spend slipped through undetected, and controllers were left reacting to compliance exceptions after the fact.

Workflow diagram · grounded in source
1
Employee submits expense
Trigger
An employee submits an expense.
▾ source quote
“Imagine an employee submits a flight expense and the receipt includes a seat upgrade.”
2
Policy Agent evaluates transaction
Ai action
The Policy Agent evaluates every transaction and recommends approval, rejection, or review, showing which policy rules it relies on.
▾ source quote
“It evaluates every transaction and recommends: Approval / Rejection / Review. Crucially, it shows which policy rules it's relying on, and flags where those rules are outdated or unclear.”
3
Gap escalation to controller
Routing
When a policy rule is absent or unclear, the Agent escalates the expense and surfaces the gap to the controller.
▾ source quote
“if seat upgrades aren't explicitly addressed, the Agent escalates the expense and surfaces the gap”
4
Controller updates policy
Feedback loop
The controller adds refined rules that evolve the policy from a static PDF to a living document.
▾ source quote
“The Controller updates the policy: "Seat upgrades <$200 are approved only for specific roles." This evolves your policy from a static PDF to a living, breathing document. Private notes like "VP level and above" guide the Agent without exposing internal …”
5
Automatic in-policy approval
Output
The Policy Agent automatically approves transactions with no queue, no reminder, and no bottleneck.
▾ source quote
“the Policy Agent automatically approves it — no queue, no reminder, no bottleneck”
6
Human review of exceptions
Human review
Reviewers focus only on the 10-15% of transactions that actually require judgment.
▾ source quote
“reviewers focus only on the 10-15% of transactions that actually require judgement”
Reported outcome

More than 1,000 finance teams adopted the Policy Agent, reclaiming 4-5 hours per week from manual reviews and catching 7x more out-of-policy spend, with reviewers now focused only on the 10-15% of transactions that require judgment.

Reported metrics
finance teams using Policy Agent1,000+
Hours reclaimed per week from manual reviews4-5 hours per week
Out-of-policy spend caught7x more
Transactions requiring human judgment10-15%
Reported stack
RampRamp Policy Agent
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Source
https://ramp.com/blog/ramp-policy-agent-ga-launch
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

More than 1,000 finance teams adopted the Policy Agent, reclaiming 4-5 hours per week from manual reviews and catching 7x more out-of-policy spend, with reviewers now focused only on the 10-15% of transactions that re…

What tools did this team use?

Ramp, Ramp Policy Agent.

What results were reported?

finance teams using Policy Agent: 1,000+; Hours reclaimed per week from manual reviews: 4-5 hours per week; Out-of-policy spend caught: 7x more; Transactions requiring human judgment: 10-15% (source-reported, not independently verified).

How is this finance ops AI workflow structured?

Employee submits expense → Policy Agent evaluates transaction → Gap escalation to controller → Controller updates policy → Automatic in-policy approval → Human review of exceptions.

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