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.
One REST call, one deterministic verdict
Upload the PDF. The API returns INTACT, MODIFIED, or INCONCLUSIVE with named markers — in about three seconds.
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 verify the figures. They do not verify 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.
Five forensic layers, one deterministic verdict
Every PDF we receive passes through the same structural pipeline — no model training, no thresholds to tune.
Metadata analysis
Creation and modification timestamps, producer and creator fields, XMP metadata — the first layer exposes basic tampering.
File structure
Xref tables, trailer chain, incremental updates. Any edit after export leaves a structural fingerprint here.
Digital signatures
Signature chain integrity and post-signature modifications produce deterministic markers. Certainty-level signal.
Content integrity
Fonts, objects, embedded content, page assembly. Multi-session edits and inserted objects are visible at this layer.
Verdict with markers
Deterministic output: INTACT / MODIFIED / INCONCLUSIVE, with named markers for every finding — suitable for audit trail.
Repair estimate and adjacent claim PDFs we check
Every type listed below is analyzed at the structural file layer — not the rendered image.
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 verification
Line arithmetic across the estimate (parts + labour + paint + tax → subtotal → grand total) is verified 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.
Two HTTP calls to verify any repair estimate
Buyers can skip this section — developers, the integration is two HTTP calls.
Step 1 — submit the PDF
curl -X POST https://api.htpbe.tech/v1/analyze \
-H "Authorization: Bearer $HTPBE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://your-storage/body-shop-estimate.pdf"}'Step 2 — read the verdict
{
"id": "r1e2p3a4-5i6r-7e8s-9t9z-r1f2g3h4i5j6",
"status": "modified",
"modification_confidence": "high",
"modification_markers": [
"Spreadsheet producer detected (Microsoft Excel)",
"Two cross-reference tables — incremental update",
"Modification date 2 weeks after creation date"
],
"producer": "Microsoft Excel",
"creator": "Mitchell Cloud Estimating (original)",
"creation_date": 1707091200,
"modification_date": 1708300800,
"has_digital_signature": false,
"xref_count": 2,
"has_incremental_updates": true
}Original came from Mitchell Cloud Estimating. Then two weeks later it was opened in Microsoft Excel and re-saved — adding a second xref table. Verdict: modified at high confidence. The shop or claimant edited a real estimate after Mitchell issued it — likely to bump line items before submitting to the carrier.
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 verified 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
Frequently asked questions
Related solutions and guides
Insurance Claims
Repair estimate + medical bill + proof-of-loss forensics for claims-ops and SIU teams.
Medical Bill Tamper Detection
Sister page — same forensics for medical bills attached to health and disability claims.
Fake Receipt Detection
Receipts attached to property and travel claims as supporting documentation.
Secure your workflow
Create your account — API key on signup, free test environment on every plan.
From $15/mo. No sales call. Cancel any time.