VerifyReceipt gives insurers one evidence-first workflow for claim documents: read the file, catch the problems that matter, surface the matched prior document when it exists, and tell the reviewer exactly what to check next.
The forensic layer between submission and payment. We started with travel because it is the hardest document verification problem in insurance.
Live review context
The strongest document issues rise to the top fast, with enough signal for a human to decide what deserves a real read.
Duplicate replay
Open the matched document, compare layout and totals, and route the case with an audit-ready explanation.
Claim-fit mismatch
Turn supporting paperwork into a reviewer-ready case file instead of a slow manual transcription exercise.
Tamper cues
Surface the exact areas and evidence trail a human needs before making the payout call.
Reads
Dates, totals, providers, line items, and document context.
Compares
Prior submissions, invoice references, layouts, and claim-fit cues.
Routes
Clean documents forward and uncertain ones into reviewer-ready workspaces.
The operating tension
Most insurers still need a human to decide whether a claim document is clean, questionable, duplicated, or structurally weak. That makes the document itself an operating-cost problem long before it becomes a confirmed fraud problem.
Lower-value claims often get paid on weak supporting documents because investigating every invoice, receipt, or screenshot costs more than the payout.
Manual review of invoices, quotes, receipts, and specialist paperwork is slow, expensive, and impossible to scale across every claim queue.
When every document looks equally uncertain, clean claims get held up, customers wait longer, and operations teams stay under pressure.
The document itself is the bottleneck. A medical invoice, repair quote, builder estimate, or hotel folio is still too often handled with guesswork, local knowledge, or over-escalation instead of a repeatable evidence workflow.
Show, don't tell
These are reconstructed, public-safe examples based on real analysis patterns. The goal is not to shame a reviewer. It is to show how the right document workflow makes uncertainty visible and actionable before payout.
Duplicate replay
Public-safe reconstructed example based on duplicate replay patterns seen in real claims workflows.
Earlier submission
Born-digital PDF
New submission
Uploaded PDF
What a quick human read might miss
Both files are clean-looking hotel folios with the same merchant and total. Without comparison, the second one can pass as routine reimbursement paperwork.
What VerifyReceipt surfaces
The workflow links the earlier matched document, keeps the repeated reference visible, and shows that the second submission shifted only the stay dates while preserving the rest of the layout.
Why that matters operationally
This becomes a reviewable duplicate question instead of a vague suspicion. The reviewer can open both documents and explain the escalation with evidence.
What the reviewer does next
Open both documents, compare layout and chronology, then route the claim only if the difference is legitimate and well supported.
Public note: these document views are reconstructed and redacted for the website. They are shaped from real signal patterns, but not copied from live customer claims files.
Real signals
These are real signal patterns from document analysis. Redacted and generalized, but drawn from actual forensic output across claim documents.
A scanned receipt had 102 characters of digital text placed on top of the image, covering only the product serial number section. Legitimate scans do not have selective text overlays.
PDF had no producer, creation date, or modification date. Legitimate scanned documents always retain scanner metadata.
Page measured 17.2 x 24.4 inches instead of A4 or Letter. Indicates digital creation, not physical scanning.
All examples are redacted from real analysis patterns. VerifyReceipt does not make payout decisions. It surfaces evidence and routes uncertain cases into human review.
Live demo
Redacted output shaped from real analysis patterns across claim documents. The goal is not a black-box score. It is a clear explanation of what to check, why it matters, and what to do next.
Capability map
This is not just OCR. The platform reads the document, checks whether it fits the claim, flags reuse or manipulation, and turns the result into an explainable workflow your team can actually act on.
01
Extract provider, dates, totals, currencies, line items, and document metadata so the reviewer starts with facts instead of manual transcription.
02
Flag totals that do not add up, dates that do not make sense, and amounts or countries that do not fit the claim context cleanly.
03
If the same PDF, a near-duplicate document, or the same invoice reference appears again, VerifyReceipt flags it and shows the earlier submission to compare.
04
Use document and image forensics to highlight flattening cues, screenshot laundering, suspicious regions, and authoring inconsistencies.
Workflow
Upload through API, dashboard, or batch ingest
Documents enter the same verification pipeline whether they arrive one at a time or in bulk.
Run the evidence stack
Forensics, extraction, duplicate checks, and claim-fit validation produce one evidence-led case file.
Highlight what matters
The portal surfaces the strongest issues first, including matched prior documents and what the reviewer should inspect.
Resolve in review when needed
Operators open the source file, compare evidence, apply corrections, refer, or adjudicate with a clear trail.
Report, callback, and audit
Results feed reporting, webhooks, and future rollout decisions without losing governance context.
What comes back
Use cases
Travel documents are the hardest proof point. The same intelligence stack extends into health, motor, property, and specialist-heavy workflows without changing the core review model.
Travel
Foreign medical invoices, hotel folios, disruption receipts from dozens of countries.
Thai clinic invoices, Bali pharmacy receipts, European hospital bills
Health
Provider receipts, treatment invoices, specialist documents.
GP invoices, physiotherapy receipts, specialist referral chains
Motor
Repair quotes, towing invoices, rental receipts, parts documents.
Panel beater quotes, windscreen invoices, hire car receipts
Property
Builder invoices, make-safe documents, restoration quotes.
Emergency plumber invoices, builder scope of works, flood restoration
Workers comp
Specialist referrals, treatment invoices, supporting expenses.
Orthopaedic reports, rehab invoices, travel-to-treatment receipts
Why now
The signals coming out of claims news are consistent across lines of business: AI-edited evidence, catastrophe-driven document surges, repair invoice scrutiny, and growing pressure to justify automated decisions.
Insurance Business
Claims teams need workflow evidence that shows where to look, not just a generic fraud score.
The real threat in claims may not be dramatic fake evidence. It may be ordinary files nudged just enough to change an outcome.
Insurance Business
Storm, wildfire, and surge claims create bulk invoice and estimate review pressure long before settlement is finished.
When storms and wildfires dominate insured loss, the operational problem is not just catastrophe severity. It is also the flood of documents that follows.
InsuranceFraud.org
Motor and property teams need cleaner invoice comparison, arithmetic checks, and repeat-document detection.
Motor and property document quality is becoming a regulatory issue, not just a claims best practice.
Insurance Business
Evidence-first review trails matter more as AI governance expectations harden.
Insurance AI is moving into a more scrutinized era, and that favors evidence-first workflows over black-box decisioning.
Insights
We track the claims news cycle and turn it into platform-relevant analysis: AI-edited evidence, contractor scams, catastrophe document surges, healthcare enforcement, repair invoice abuse, and explainable AI controls.
News-backed analysis
Property claims have always relied on invoices, receipts, and scope documents. What changed is how cheaply those documents can now be fabricated.
Based on January 2026 reporting and industry analysis about AI-generated receipts in property claims.
Read articleThe industry is moving from a world of suspicious photos to a world of convincing synthetic evidence.
Based on January 2026 insurance and legal analysis of synthetic claim images and manipulated media.
Read articleCarrier pressure
When claims get more complex and staff stay pressured, insurers need a better way to decide which documents deserve human time.
Based on February and March 2026 reporting on carrier claims complexity and cost pressure.
Read articleNext step
Start with a focused pilot on historical claim documents, quantify the issues your team actually sees, and decide how the workflow should look before you scale production.
hello@verifyreceipt.ai | Sydney, Australia | AWS ap-southeast-2