# Receipt Fraud Detection > 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. **Canonical URL:** https://tampercheck.ai/receipt-fraud-detection --- ## 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 TamperCheck detects ### 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. ## 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. - **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 ## Frequently asked questions ### What is receipt fraud detection? Receipt fraud detection is the automated forensic analysis of a submitted receipt to determine whether it was genuinely issued at the point of sale - catching AI-generated receipts, edited totals, and duplicate submissions before they enter an expense, reimbursement, or claims decision. ### Can TamperCheck detect AI-generated receipts? Yes - this is the core reason it exists in 2026. AI-generated receipts look flawless to the eye but carry image-generation signatures, missing capture provenance, and rendering patterns that differ from a real camera or POS printer. TamperCheck's computer-vision layer detects these even when the receipt reconciles arithmetically. ### Does it work on phone photos of paper receipts? Yes. Image-based checks - noise analysis, lighting and capture consistency, AI-generation detection - run on photos and scans, while structure and metadata checks run on digital PDF receipts. TamperCheck applies the right check set for each file type automatically. ### How does this fit into an expense or AP workflow? One API call per receipt at submission returns a JSON verdict in about a minute, so suspicious receipts route to review before reimbursement rather than surfacing at month-end reconciliation. There's also a dashboard for manual checks, and $5 in trial credits to evaluate accuracy on your own receipts. ### How is this different from OCR-based expense tools? OCR reads what a receipt says - vendor, date, total - and assumes the pixels are genuine. It has no opinion on whether the receipt was ever real. Fraud detection asks the separate question of whether the document was authentically captured or generated, which is exactly the gap AI-generated receipts exploit. ## Related use cases - https://tampercheck.ai/use-cases/insurance-claims - https://tampercheck.ai/use-cases/hr-hiring - https://tampercheck.ai/use-cases/kyc-identity-verification ## Compare with alternatives - https://tampercheck.ai/compare/ocrolus-vs-tampercheck-ai - https://tampercheck.ai/compare/inscribe-vs-tampercheck-ai --- Try TamperCheck free: https://tampercheck.ai/dashboard · Docs: https://tampercheck.ai/docs · Pricing: https://tampercheck.ai/pricing