Use case assessment
Is AI expense approval worth building?
Your policy is already rules. Automating it isn't AI, and making it a model makes it worse.
An expense policy is a written set of thresholds and categories. Encoding it is automation, and it should stay auditable because employees will contest decisions.
What it usually means
Automatically approving or flagging employee expense claims against policy.
Is it genuinely AI?
The approval decision is not. Your policy says what is claimable, up to what limit, with what evidence. Those are thresholds. Encoding them is automation, and automation is the right answer because an employee whose claim is rejected will ask why.
"The policy says meals over this amount need a manager's approval" is an answer. "The model scored it 0.31" is not, and it will escalate.
Receipt reading is a different matter. Extracting amount, date, merchant and category from a photographed receipt is genuinely a model problem, and a well-solved one.
What the simpler version looks like
Rules for the decision, extraction for the input, a review queue for anything the rules cannot place, and sampling for audit.
Worth noting the sampling: a system that auto-approves everything within policy and never checks removes the deterrent effect, which is often what the control was actually for.
What it costs to run
Receipt extraction accuracy degrades with photo quality and merchant format changes. Budget for a low-confidence path. The rules cost a config change when policy changes, which is the point.
Where it sits under the EU AI Act
Expense processing is not an Annex III high-risk use in itself.
One boundary worth knowing: worker management is an Annex III area. A system that only checks claims against policy is administrative. A system that scores employees for suspicion, or feeds into performance evaluation, moves toward that classification — and that is a feature that gets added later without anyone reconsidering the risk position.
When AI is worth it here
For the extraction, at volume. Buy it; this is a mature category.
Not for the decision. If your policy is too ambiguous to encode as rules, the problem is the policy, and a model will make its ambiguity harder to see rather than easier.
Common questions
Does expense approval need AI?
The decision does not. An expense policy is a set of thresholds and categories, and encoding it is automation. That matters because employees contest rejections, and a rule gives an answer while a model score does not.
Where does AI genuinely help with expenses?
Receipt extraction: reading amount, date, merchant and category from a photograph. That is a model problem and a well-solved one. Buy it rather than building it.
Is expense automation high-risk under the EU AI Act?
Not in itself. But worker management is an Annex III high-risk area, and a system that scores employees for suspicion or feeds performance evaluation moves toward that classification. That is usually a feature added later without the risk position being revisited.
Turning a per-client survey into one framework — encoding policy once, correctly, rather than rebuilding the judgement each time.