A practical guide for anyone who approves or processes expense reports and has zero interest in eyeballing six hundred restaurant receipts a month.
Reading every line of every expense report doesn’t scale, and it never really did. The fix is triage: sort the pile into obvious, obviously wrong, and worth a second look, before you spend real time on any of it. AI is good at that sorting job, which is exactly what a tired approver or an overloaded AP team actually needs. Early customers using Ramp’s AI agents for controllers reported 99% accuracy in expense approvals compared to human reviewers, and most policy violations aren’t fraud at all: a 2025 GBTA and ALTOUR survey found the top reason travelers break policy is simply that they don’t know the rules exist.
Most expense report reviews are theater. You open it, scroll past the airfare and the hotel folio, glance at the total, and click approve, a rubber stamp with your name on it and not much else behind it.
Here’s the part nobody says out loud: reading every line of every expense report was never actually possible at scale, honestly. A manager approving twenty reports a month, or a controller processing two thousand, cannot scrutinize each receipt the way a dedicated auditor would. Something gives, and it’s usually diligence. I’ve sat across from enough controllers and office managers in the workshops I run, we’ve now trained more than 2,000 non-technical professionals through corporate sessions and AI bootcamps, to know almost none of them think they’re doing a great job at this part. They know they’re skimming. They just don’t see another option.
The good news, and I don’t say this to let anyone off the hook, is that most policy violations aren’t malicious. A 2025 survey of North American travel managers from GBTA and ALTOUR found the single most common reason travelers break policy is that they simply haven’t read it or don’t know the rules exist.[1] Call it an attention gap more than a fraud problem, and it happens to be exactly the kind of thing AI catches without anyone needing to play detective. The fraud that does exist isn’t trivial either: expense reimbursement schemes sit inside asset misappropriation, the category behind 89% of all occupational fraud cases in the ACFE’s most recent Report to the Nations.[2]
Think about the incentives for a second. A busy approver clicking through forty reports before lunch is triaging their day the only way they know how, fast, and I get it, I do the same thing with my own inbox most mornings. The trouble is that fast and blind look identical from the outside. Nobody can tell the difference between an approver who genuinely checked a report and one who scrolled past it, until months later when someone finally asks why a $600 airport lounge charge sailed through without a second look.
You don’t need to become an internal auditor overnight. You need a system that flags the handful of expenses actually worth your attention, so you spend thirty seconds on those instead of thirty minutes on all of it.
AI expense review, whether it’s a general chat assistant or a purpose-built platform, is basically a pattern matcher. It compares what’s in front of it against a policy, a history of past submissions, and known red flags, then tells you what deserves a second look.
Picture a sales rep who submits three nearly identical hotel receipts across two different trips, each one just under the $200 approval threshold. On its own, none of those looks wrong. Lined up against a full quarter of that rep’s spending, the pattern is obvious in about two seconds, which is exactly the kind of comparison a human reviewer almost never has time to make by hand.
None of this takes an accounting degree to understand once it’s flagged for you. The AI does the tedious comparison work. You make the judgment call on the few items that actually need one.
There are two ways to get AI into your expense review process, and most people only know about one of them.
The first is general-purpose AI: ChatGPT or Claude, fed a CSV export or a batch of receipt photos and asked to flag anomalies against a plain-language version of your policy. It’s free, or close to it, and it works today with whatever spreadsheet you’ve already got. Honestly, it’s a solid enough place to start if there’s no budget for new software yet. The catch is you’re doing the exporting and the asking manually, every single time. We walk through the mechanics of that kind of prompt-and-data workflow in our guide to ChatGPT for financial analysis, and it applies here almost word for word.
The second path is expense platforms with AI already built in. Expensify’s receipt auditing checks every submission against policy and flags duplicates and mismatches automatically as they come in.[4] Brex lets admins build if-this-then-that policy rules that approve, flag, or block spend in real time, right at the point of purchase instead of after the fact.[5] SAP Concur’s Intelligent Audit pairs AI with human auditors to check every expense report for duplication, errors, and out-of-policy spend.[6] Ramp went further and released AI agents built specifically for controllers, and early customers reported 99% accuracy on expense approvals compared to what a human reviewer decided.[3]
Cost is part of this decision too. Dedicated AI audit features usually sit behind a paid tier or an add-on SKU, so a five-person team spending $40,000 a year probably doesn’t need to buy an enterprise audit module. A fifty-person company processing thousands of expenses a month probably does, because the manual alternative ends up costing more in staff hours than the software ever will.
My honest take, and I’ll just say it plainly: if you’re already paying for one of these platforms, turn on its built-in AI first. It’s watching every transaction as it happens, which beats remembering to paste things into a chat window. If you’re not on one of these tools yet, ChatGPT or Claude with a clear prompt gets you most of the value at zero extra cost while you figure out whether switching is worth it.
If you approve reports for your own team but don’t own the company’s finance software, you probably can’t flip on some enterprise AI audit feature. You need something you can do this afternoon, with the tools already sitting on your desktop.
Export the batch of reports, most systems will spit out a CSV or PDF, and paste the details into ChatGPT or Claude along with your team’s actual expense policy written in plain language. Ask it to flag anything that violates the policy, anything duplicated, and anything unusual compared to that employee’s normal spending pattern.
Sample prompt: “Here’s our expense policy and a list of this month’s submitted expenses. Flag anything over the meal or hotel limit, anything submitted twice, anything missing a receipt above $25, and anything unusual for this employee’s normal spending. Explain each flag in one sentence.”
Expect some pushback the first time an employee gets flagged for something that turns out to be fine. That’s normal, and honestly it’s a decent sign: the system is looking closely enough to ask questions instead of rubber-stamping everything. Explain the flag, get the context, move on. You’re not aiming for zero flags. You’re aiming for flags worth a thirty-second conversation.
This won’t catch everything a dedicated platform would, but it catches the obvious stuff, which is most of it. And it takes about the same fifteen minutes whether you’re reviewing five reports or fifty.
If you’re the one actually running expense operations, accounts payable, controller, or an office manager wearing six hats, you’ve got more room to fix the root problem. You can change the system instead of working around it.
Start by writing the policy the way you’d actually explain it out loud to a new hire. Vague language like “reasonable and customary” gives an AI reviewer, and a human one, nothing concrete to check against. Give it real numbers instead: a meal cap, a hotel rate ceiling, an actual dollar threshold for when a receipt is required. If you need help turning a messy policy document into something this clear, our walkthrough on using AI to write SOPs is built for exactly this kind of rewrite.
Then decide which layer of AI you actually need. If you’re already on Expensify, Ramp, Brex, or Concur, turn on their audit or policy-enforcement features before building anything custom on top. If you’re still managing this out of QuickBooks and spreadsheets, our guide to Claude for small-business workflows covers connecting AI tools to the stack you already have, instead of ripping it out for something new.
When you’re comparing platforms, ask each vendor the same blunt question: what happens to a flagged expense after the AI catches it? Some tools just highlight the flag and leave the decision entirely in your lap. Others route it through a structured approval chain with an audit trail attached. Once you’re processing enough volume that a spreadsheet of flags becomes its own management problem, the second kind is worth paying more for.
Either way, the AI’s real job here is sorting. It works out to three piles: clearly fine, clearly wrong, and needs a human. Keep that last pile small enough that someone actually looks at it.
I watched this happen during a workshop I ran for a mid-size services company: a manager approved an entire month of expense reports in about four minutes flat, clicking through without opening a single receipt. That’s not really an AI problem, honestly. AI review just makes the habit more visible, because now there’s a flag sitting right there that got dismissed without so much as a glance.
The most common mistake is treating every flag as noise. If your AI reviewer surfaces fifteen items a week and you wave all of them through because you’re slammed, you’ve built an expensive way to ignore problems instead of catching them. There’s an opposite mistake too, just less common: treating every flag as gospel and rejecting anything the AI didn’t fully understand, like the client dinner that ran long for a genuinely good reason.
There’s a third mistake worth naming: rolling out an AI reviewer and never telling anyone. Employees who don’t know a system is checking their submissions can’t calibrate their own behavior, and usually the first time they learn about it is when they get flagged for something they didn’t realize was a problem. Announce the change, share the policy in plain language, and the number of flags tends to drop within a month or two simply because people finally understand what’s being checked.
Let’s be honest, most people just want the review to be over, and AI can quietly become the thing that lets them skip the last uncomfortable step, actually deciding whether a flagged expense is fine. Don’t let it. Treat a flag as the start of a thirty-second conversation, never the whole conversation on its own.
AI doesn’t know that your VP always splits a hotel bill because their partner tagged along, or that a team dinner ran expensive because a client’s flight got delayed and everyone waited three hours at a restaurant. It only knows what’s in the data and the policy, and it will flag those things every time.
This is also where a lot of AI adoption goes sideways more broadly, not just in expense review. Some teams hand over too much trust and stop paying attention. Others distrust the tool completely and it never gets used for much beyond a novelty. The ones getting this right treat AI as a fast first-pass reviewer working alongside a person. It doesn’t replace anyone.
A flag isn’t an accusation. Think of it as a question instead. The human reviewer’s job shrinks to the handful of cases where context actually matters, which is a far better use of anyone’s afternoon than reading six hundred lines of hotel folios.
The teams that get the most out of this don’t hand every decision to AI, and they don’t ignore it either. They let it sort, and they keep making the calls that actually need a human. That’s really the whole point of using it in the first place.
Yes, both can read uploaded images and PDFs directly, and they can also work from a CSV or spreadsheet export of your expense data. For scanned receipts, quality matters a lot: a blurry phone photo gives worse results than a clean scan or the auto-captured version from your expense app. If you’re scanning paper receipts yourself, a flatbed scan or a well-lit photo taken straight-on works far better than a crumpled receipt shot at an angle in bad lighting.
It depends on your company’s data policy and which plan you’re using. Business and enterprise tiers of both tools generally don’t train models on your data, but check your organization’s approved-tools list before pasting in anything with employee names, card numbers, or sensitive vendor details. When in doubt, strip identifying information first.
Yes, though the depth varies quite a bit. Expensify audits receipts and flags duplicates and policy issues automatically.[4] Brex lets you build real-time policy rules that approve or block spend as it happens.[5] Ramp has released dedicated AI agents for controllers that review and approve expenses directly.[3] SAP Concur pairs AI with human auditors for a hybrid review process.[6]
No, and I’d be a little suspicious of anyone telling you otherwise. AI is good at sorting volume and catching patterns, but it isn’t good at judgment calls that need context, like why a client dinner ran over budget. It removes the tedious first pass so the humans can focus on decisions that actually need one. Think of it as a fast, literal-minded assistant that never gets tired of comparing numbers. It won’t replace anyone’s judgment.
The same way you’d stop them gaming a human reviewer: vary what gets a closer look, keep some manual spot-checks even on clean-looking reports, and update your policy language and thresholds periodically instead of leaving them static for years. A system that only ever checks the same three things becomes predictable, and predictable systems get worked around eventually.
I researched this piece by pulling directly from Ramp, Expensify, Brex, and SAP Concur’s own product pages on how their AI audit and policy-enforcement features actually work, plus fraud data from the ACFE’s Report to the Nations (via The Bonadio Group’s analysis) and GBTA and ALTOUR’s 2025 corporate travel policy survey. Every statistic and product claim below links back to where I found it.
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