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For claims operations

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.

~3 sec
per document
35 checks
forensic layers
From $15
per month
1,500+
docs / month on Growth
Scope

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.

How it looks

One REST call, one deterministic verdict

Upload the PDF. The API returns INTACT, MODIFIED, or INCONCLUSIVE with named markers — in about three seconds.

What this looks like

Insurance Claims document fraud in 2026

Three real fraud mechanics we catch at the structural PDF layer.

01

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.

02

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.

03

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

$308B+
lost to insurance fraud globally every year
10–15%
of all claims involve some form of document fraud
~3 sec
per document verification

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.

Results in under 3 seconds30 to 1,500+ documents/monthFrom $15/mo
How it works

Five forensic layers, one deterministic verdict

Every PDF we receive passes through the same structural pipeline — no model training, no thresholds to tune.

01

Metadata analysis

Creation and modification timestamps, producer and creator fields, XMP metadata — the first layer exposes basic tampering.

02

File structure

Xref tables, trailer chain, incremental updates. Any edit after export leaves a structural fingerprint here.

03

Digital signatures

Signature chain integrity and post-signature modifications produce deterministic markers. Certainty-level signal.

04

Content integrity

Fonts, objects, embedded content, page assembly. Multi-session edits and inserted objects are visible at this layer.

05

Verdict with markers

Deterministic output: INTACT / MODIFIED / INCONCLUSIVE, with named markers for every finding — suitable for audit trail.

Document types

PDF document types we verify for insurance claims

Every type listed below is analyzed at the structural file layer — not the rendered image.

Repair invoice PDFMedical bill PDFHospital discharge summary PDFPolice report PDFProof of loss PDFAdjuster estimate PDFPrescription PDFDiagnostic report PDF
What HTPBE checks

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

FAQ

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.

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.