Forging a convincing receipt used to take real skill. It doesn’t anymore.
Anyone with a phone can now use AI to generate a receipt for a restaurant that exists, on a date that makes sense, with a total just under an approval threshold. The best AI-fake receipts include details people associate with authenticity, like a coffee stain or a crease down the middle of the paper. The visual “tells” that auditors were trained to spot, like misaligned logos or a mismatched font, are now easy for an AI model to get right.
In early 2025, no expense automation tools were regularly flagging receipts with AI-generated images. By May of 2026, our platform data indicated AI-generated fakes made up 70.8 percent of fake receipt flags. In the one year from May 2025 to May 2026, our models detected 1,471 AI-generated receipts.
Together, those receipts represented $148,143 in expense claims and came from 745 employees across 174 companies. Although the individual amounts were small, averaging $101 and a median of about $32, what was important was the pattern we were seeing and the change in the methods being used.
The question finance leaders often ask me is a fair one: If the receipt’s picture is perfect, what is left for our agentic AI to detect? More than you might expect.
What AI finds in the receipt image
The first examination is forensic. Generated images carry a history. Metadata fields indicate the tool that was used to produce them. A photograph of a piece of paper has a compression history, a light source and a focal plane. A rendered image has none of that, and simulating all of it convincingly is hard.
The rendering itself leaves its own marks. Image models remain weak at generating dense, small type. Repeated characters drift, and glyph shapes vary where a thermal printer would print them identically. The visual “noise” that mimics faded print sits on top of the text rather than inside it.
None of this proves anything alone. Together, however, these signals turn an unremarkable receipt into one worth a closer look.
What AI finds in the transaction
The second layer of examination has little to do with how the receipt looks. Real receipts are outputs of a real system, so they obey arithmetic. Line items add up to a subtotal, the tax matches the rate in that jurisdiction, and a tip is a plausible percentage of the right base. Fabricated receipts often fail this check because a language model writes numbers that look correct, but don’t reconcile.
Real receipts also obey a template. A given merchant prints from a handful of point-of-sale configurations, with a recognizable store number format, terminal identifier, time stamp convention and footer. When you hold millions of receipts from that merchant, a fabricated one stands out beside the genuine ones. It is rarely wrong in any single place. It is wrong relative to the entire population.
Then there is corroboration. Card data, travel itineraries, calendar entries and colleagues’ expenses on the same trip either support the transaction or they don’t. A dinner receipt that never ran through a card feed is an even stronger signal than a font that looks slightly off.
Employee expense behavior closes the loop. The most common pattern we see is repetition. One underlying image, with the amount and date edited, is submitted just outside a duplicate-check window.
Layered reviews are the best answer to AI-generated fraud
No single test of a receipt is reliable enough to act on by itself. A forger can strip the metadata from an image and ensure the arithmetic adds up. Intent varies, too, as some fabricated receipts are simply employees reconstructing genuine expenses after losing the original receipt.
Detection must combine the full set of weak signals, score them together and provide an explanation. A system that returns a confidence score and the full audit context lets a human reviewer investigate and determine intent. A system that returns a pass or fail forces auditors to guess, introducing human bias and error.
Three changes finance leaders should make today
Making these three changes before the next quarter closes will improve your chances of catching an AI-generated receipt.
1. Audit every report before payment. Most enterprises review 10 percent to 20 percent of expense reports, selected by rules or sampling. Partial coverage is exposure.
2. Write down the policy. Many expense policies never state whether recreating or manipulating receipts is permitted. Make it clear, so intent is handled consistently.
3. Corroborate instead of inspect. Any control that depends on a person recognizing a fake by eye is already obsolete.
Receipt images will keep getting easier to fake, but there will always be evidence to gather. A real dinner leaves a card authorization, a calendar entry, a merchant record that matches thousands of others and sometimes a colleague who expensed the same meal. Across the receipts we see at AppZen, an AI-generated image might pass visual inspection, but fall apart everywhere else. That is where finance teams should be looking. And it’s why we use AI agents to find the details and patterns people cannot. An AI model can generate a picture of a dinner. It still cannot generate the dinner itself.
