Receipt fraud detection built for the AI-generated era
In 2026, AI-generated fakes became the majority of flagged expense fraud - photorealistic receipts with real vendor names, plausible line items, and amounts tuned to slip under auto-approval limits. TamperCheck runs 130+ forensic checks on every receipt - photo, scan, or PDF - and returns a plain-English verdict in about a minute.
The problem
Why fake receipts stopped being obvious
For years, a fake receipt gave itself away: a crooked logo, the wrong font, arithmetic that didn't add up. Reviewers learned the tells, and most fabricated receipts got caught on sight.
That era is over. A wave of free tools now generates photorealistic receipts from a one-line prompt, complete with real vendor branding, believable line items, tax lines that reconcile, and even paper texture and fold shadows. There is nothing to spot with the naked eye - which is why a growing share of expense fraud now clears review.
The amounts are deliberately unremarkable, too: fabricated receipts cluster just below common auto-approval thresholds, so they never reach a human at all. The result is a steady leak - small individual claims, submitted at volume, that traditional review was never built to catch.
What we detect
Forensic signals that expose fraud
AI-generation signatures
Fully synthetic receipts carry the noise, compression, and rendering patterns of an image generator rather than a real camera or point-of-sale printer. TamperCheck's computer-vision layer detects these signatures even when the receipt looks perfect.
Missing capture provenance
A genuine phone photo of a receipt carries camera metadata and a natural imaging history. Generated and screenshot-exported receipts arrive stripped of it, or with metadata that contradicts the claimed capture.
Total and tax arithmetic
Line items, subtotal, tax, and total must reconcile. Edited receipts - where a fraudster changes one number in an image editor - break the arithmetic or leave a re-typed figure that doesn't match its neighbours.
Font metrics and editing traces
Altered totals are re-typed in a near-match font. Character-level font-metric comparison flags fields that don't match the rest of the receipt, and PDF receipts carry editing-tool fingerprints in their structure and metadata.
Duplicate and near-duplicate detection
The same receipt resubmitted with a changed date, or one template reused across many claims, is a classic expense-fraud pattern. Perceptual hashing catches reuse that a line-by-line reviewer never connects.
Vendor and date consistency
Vendor names, addresses, dates, and currency are checked for internal consistency and against the claim they support - catching plausible-looking receipts assembled from mismatched parts.
The solution
How TamperCheck detects receipt fraud
TamperCheck treats every receipt as a forensic object, not a total to be read. Image forensics, structural analysis, and AI adjudication run in parallel, and the verdict comes back with specific findings - so the check happens at submission, not months later at reconciliation.
How it works
- Upload via API or dashboard - single endpoint, single file, verdict in about a minute
- 130+ forensic checks run automatically on every receipt
- Works on photos, scans, and PDFs - no template library to maintain
- Catches AI-generated receipts the eye and threshold rules miss
- Plain-English findings your finance or ops team can act on directly
- $0.50 per document - no subscriptions, no minimums, zero document storage
FAQ
Common questions
See it working on your documents
Start with $5 in free credits - no contract, no card required. Upload your first document and get a verdict in about a minute.