Staged claims don’t announce themselves.Tampered invoices do
Most claim fraud ends with a document: an inflated repair invoice, a fabricated medical bill, a police report with adjusted line items. Structural forensics catches the edit before the payout.
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 PDF, we’re the most specific tool for it.
One REST call, one deterministic verdict
Upload the PDF. The API returns INTACT, MODIFIED, or INCONCLUSIVE with named markers — in about three seconds.
Insurance Claims document fraud in 2026
Three real fraud mechanics we catch at the structural PDF layer.
Inflated repair and medical invoices
Line items, subtotals, or totals edited on otherwise legitimate invoices. Arithmetic across the document stops reconciling when one figure changes.
Fabricated receipts and proof-of-purchase
Receipts generated through templates or AI tools to support a claim for items that were never purchased. Producer signatures and font subset patterns expose the fabrication.
Altered medical certificates
Diagnosis dates, injury descriptions, or practitioner details modified on genuine medical documents. The structural layer records the change even when the visual layout is perfect.
The scale of the problem
The verification gap
KYC platforms verify the document. HTPBE verifies the file.
Two different checks — both matter.
OCR-based claims platforms read what the document shows. They don’t know whether the invoice was edited after creation. SIU investigators work on pattern matching across claims — that’s slow and downstream. HTPBE verifies each document at intake, before approval, at the file-structure layer the visible content can’t hide.
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.
PDF document types we verify for insurance claims
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.
Line-item arithmetic
Row totals, subtotals, tax, and grand totals are checked for internal consistency across the invoice.
Incremental update detection
Any post-issuance edit leaves a fingerprint in the xref table and trailer chain.
Producer consistency
Real invoices come from known billing systems with recognizable producer signatures. Re-saves and fabrications change this signature.
AI-generated content markers
Receipts and documents produced through generator tools carry structural patterns distinct from authentic exports.
Font and object consistency
Injected text or tampered fields show font subset prefix shifts and object-layout anomalies.
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
Phone photos are raster images with no PDF structure to analyze. For image evidence, pair HTPBE with tools specialized in image-layer forensics. HTPBE verifies any digitally issued PDF (invoices, medical bills, proof of loss) at the structural layer.
Yes. The API is stack-agnostic — any system that accepts PDF uploads and can make an outbound HTTPS call can integrate. A pre-verification step at claim intake or document upload works cleanly.
We report MODIFIED for any post-creation edit — malicious or benign. Your SIU or adjuster decides what to do with each case, using the named markers in the response to triage quickly.
No. It’s an automated screening layer that surfaces suspicious documents for SIU review. It narrows the queue and provides a structured audit trail for every document.
Related solutions and guides
Invoice Fraud
Same underlying forensics applied to B2B vendor invoices and BEC scenarios.
Accounts Payable Fraud
AP-team angle: vendor onboarding, bank-detail change requests, supplier invoice fraud.
Invoice Tampering Detection
Technical walk-through of invoice-level structural markers.
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