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's itself a fraud signal in this context — real estimates always come from professional estimating software (Mitchell, CCC ONE, Audatex for auto; Xactimate, Symbility for property), never from a desktop tool.
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. The producer field changes from the estimating engine to whichever editor was used; the xref chain shows an incremental update.
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. The producer is Microsoft Word; the structured estimating-system metadata authentic estimates carry is missing entirely.
Multiple "shop" estimates submitted with shared fingerprints
Two or three "competing" estimates submitted to support a higher-payout claim — but the PDFs share font subset prefixes, producer signatures, or creation timestamps within minutes of each other. Cross-document analysis exposes 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 cannot tell whether the underlying PDF was issued by their own software or fabricated on someone's desktop. 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.
Producer signature mismatch
Authentic auto estimates carry the producer signature of professional auto estimating software (Mitchell Cloud Estimating, CCC ONE, Audatex Estimating). Property estimates come from Xactimate, Symbility, ACE, or similar. When the producer is Microsoft Excel, Microsoft Word, LibreOffice, Chrome Headless, or a generic PDF library, the document was authored on a desktop — it didn't come from professional estimating software.
Incremental update trail
A clean estimating-software export has one cross-reference table. Re-saves through any editor append a second xref — visible structural evidence of post-issuance editing.
Line-item arithmetic fraud detection
Line arithmetic across the estimate (parts + labour + paint + tax → subtotal → grand total) is checked row by row. Edited line items break the chain unless every dependent figure is also adjusted.
Modification timestamp gap
A real estimate issued at the time of inspection has CreationDate matching the inspection date. A weeks-or-months-later modification on a "freshly issued" estimate is a high-confidence flag for post-export editing.
Cross-estimate consistency analysis
When multiple "competing" estimates arrive together, the API surfaces producer signatures, font subset prefixes, and creation timestamps for each. Estimates from genuinely different shops produce distinct fingerprints; collusion or single-source fabrication leaves shared fingerprints.
Image-stream artefacts in fabricated headers
Fabricated estimates often paste shop logos lifted from public sites. The pasted image stream carries different compression characteristics than authentic embedded headers — a structural fingerprint of fabrication.
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
$15/mo
30 checks/mo
Manual spot-checks and integration testing
most commonGrowth
$149/mo
350 checks/mo
Active document processing pipelines
high volumePro
$499/mo
1,500 checks/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?
Secure your workflow
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