Fake Repair Estimate Detection – Catch Tampered Claim PDFs
A repair estimate is the figure your adjuster signs off – and an edited PDF is the figure your adjuster opens. Auto claims adjusters and property claims teams treat the body-shop or contractor estimate as the basis for payout. SIU teams investigate suspicious estimates after the fact. The fraudulent shops know that visual review of a PDF estimate rarely catches an edit – they bump the line items between issuance and submission, and the inflated figure becomes the figure on the cheque.
HTPBE? analyzes the structural layer of the PDF file – the layer that records every edit, even invisible ones. We don’t inspect holograms, phone photos, or ID biometrics. If your fraud problem is a digitally altered or fabricated repair estimate, we’re the most specific tool for it.
When HTPBE? returns INCONCLUSIVE on a repair estimate, that is a reason to escalate to manual review, not proof of fraud – it means HTPBE? cannot confirm the file as an untouched original.
The problem
Modern document fraud is invisible to visual review
A growing class of document fraud opens a genuine PDF, edits a balance, a date, or a beneficiary, and re-saves it. Visually nothing changes – the document passes pixel-level review, layout review, and KYC.
Structural PDF analysis reads the layers rendering engines never expose: revision history, object structure, signature coverage maps. That is where edits leave fingerprints they cannot wipe.
Common tampering patterns
- Modified balances or totals after export
- Swapped IBAN or beneficiary on invoices
- Post-signature edits on contracts
- Backdated issue and modification dates
- Fabricated documents from consumer PDF tools
What this looks like
How fake and tampered repair estimates actually look
Three real fraud mechanics we catch at the structural PDF layer.
Real estimate edited to bump line items
Authentic estimate from professional estimating software. The shop or claimant downloads it, opens it in any PDF editor, bumps a line item or adds a new operation, exports as PDF. HTPBE? flags the result as edited after issuance.
Estimate fabricated in Word from scratch
A repair-estimate-shaped PDF authored in Word using a body-shop letterhead or a contractor template, populated with a desired list of operations and parts, exported. However closely it copies a shop’s layout, the file does not come back INTACT: with nothing indicating later editing it returns INCONCLUSIVE and should go to manual review; with evidence of editing or fabrication, MODIFIED.
Multiple ‘shop’ estimates made in one sitting
Two or three ‘competing’ estimates submitted to support a higher-payout claim – but the PDFs were created within minutes of each other with the same tooling. Lining up the dates and metadata the API returns for each file can show that the ‘different shops’ all came from the same source.
The scale
Why your existing checks miss this
Estimate-comparison platforms check the figures. They do not check the file.
And SIU investigates downstream – after the cheque is in motion.
Estimate-comparison platforms (Mitchell, CCC ONE, Audatex have their own audit tooling) run rules against the data – they do not check whether the underlying PDF was edited after it was exported. SIU teams investigate suspicious claims after the fact, downstream of the adjuster decision. HTPBE? catches the repair estimate PDF the shop or claimant uploaded at the moment of intake – standalone, no estimating-platform integration, no SIU referral required.
What HTPBE? checks
Detection capabilities
Deterministic structural signals. No probabilistic scores, no model training.
An estimating-software look isn’t enough
An estimate exported directly from Word, Excel or LibreOffice, with nothing indicating later editing, comes back INCONCLUSIVE – not INTACT – however closely it copies a Mitchell, CCC ONE or Xactimate layout; MODIFIED when the file itself carries evidence of editing or fabrication.
Post-issuance edit detection
Editing a repair estimate after issuance – in a PDF editor or in other software – leaves structural evidence behind. HTPBE? flags it as MODIFIED.
Character overlay on the original
Altering a line item on an existing estimate leaves a structural trace. HTPBE? flags it without reading the amounts – it never looks at the figures on the page.
Dates that disagree
When the dates recorded inside a ‘freshly issued’ estimate disagree with each other, HTPBE? flags it – a high-confidence sign of editing after export.
Side-by-side metadata for competing estimates
When multiple ‘competing’ estimates arrive together, the API returns the producer and creator fields and the creation and modification dates for each, alongside its verdict and markers. ‘Different’ shops whose estimates were created minutes apart with the same tooling are worth a closer look.
Pasted-element detection
Fabricated estimates often paste shop logos lifted from public sites. HTPBE? detects logos, signatures and headers lifted from another document and pasted in.
Share with engineering
Wire this into your intake pipeline in under a day
Two API calls – one POST to submit the PDF, one GET to retrieve the verdict. Forward this page to your engineering team; the full API reference, quotas, and copy-paste examples in cURL, JavaScript, Python, PHP, Go, and Ruby are one click away.
Pricing
Self-serve plans, no sales call
All plans include the same forensic checks. Pick the quota that matches your monthly document volume.
manualStarter
30 credits/mo
Manual spot-checks and integration testing
most commonGrowth
350 credits/mo
Active document processing pipelines
high volumePro
1,500 credits/mo
High-volume automation and API integrations
Enterprise (unlimited, on-premise available) — see full pricing
API key on signup. Free test environment on every plan. No card required.
Customer Stories
Teams that stopped document fraud
Compliance, finance, and risk teams use HTPBE? to catch manipulated PDFs before they become costly mistakes.
Caught an invoice where the total had been changed by less than a thousand dollars. Without this I would have approved it without a second look.
Sarah M.
AP Manager
United States
We had three applicants in the same week with bank statements that looked completely fine. Two of them were flagged as modified. You simply cannot see this by reading the document — it is in the file structure.
Lars V.
Risk Analyst, Online Lending
Netherlands
Salary slips were coming with altered figures. We identified two problematic files before the placement was finalised.
Priya K.
HR Operations Lead
India
Since we started checking documents this way, we stopped two applications early in the process that would have been very difficult to reverse later.
Julien R.
Fraud Analyst, Fintech
France
Some applicants were sending PDFs that looked authentic but had been edited in ways not visible to the eye. We now ask for checked originals when something is flagged. Already saved us from a few bad decisions.
Marta S.
Compliance Coordinator
Spain
One invoice was caught because there was a mismatch between the document dates and structure. That particular case would have cost us significantly.
Tariq A.
Finance Manager
United Arab Emirates
FAQ
Frequently asked questions
Does this work with estimates from any auto or property estimating software?
How is this different from CCC ONE or Mitchell audit tooling?
Can it catch collusion across multiple ‘competing’ estimates?
What does an INCONCLUSIVE verdict mean for a repair estimate?
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