Insurance Claims Fraud Detection API
Detect altered repair estimates, inflated medical reports, and fabricated receipts before claims are paid. A single API call surfaces forensic evidence of PDF modification — at document intake, before adjuster review.
HTPBE? analyzes the structural layer of the PDF file — the layer that records every edit, even invisible ones. We don’t inspect phone photos, vehicle telematics, or claimant biometrics, and we don’t replace SIU investigation. If your claims fraud problem includes tampered or fabricated supporting PDFs (medical bills, repair estimates, receipts, proof of loss), we’re the most specific tool for it.
INCONCLUSIVE meaning is context-dependent: institutional documents (medical bills, repair estimates) → INCONCLUSIVE is a high-confidence fraud signal; consumer documents (receipts, claimant statements) → INCONCLUSIVE is the expected baseline, requires combining with other markers.
The problem
Insurance document fraud is a $308 billion problem
According to industry estimates, insurance fraud costs US carriers $308 billion annually. An estimated 25–30% of claims now involve documents that have been digitally altered — repair estimates with inflated line items, medical reports with changed diagnoses, receipts with modified amounts.
Claims adjusters process dozens or hundreds of PDF documents per week. Manual review cannot catch structural modifications that are not visible when viewing the document. A fraudster can open a legitimate repair estimate in Adobe Acrobat, change one line item, and re-save the PDF. The resulting document looks identical to the original.
HTPBE? analyzes the binary structure of each PDF for evidence of post-issuance modification. The structural traces left by editing tools cannot be removed without invalidating the document entirely.
Most common insurance document fraud patterns
- Repair estimate: add parts that were not replaced
- Medical report: change amounts or details after issuance
- Receipt: inflate labor hours or unit prices
- Expert assessment: change the damage severity rating
- Police report: alter the accident description or vehicle details
- Prescription: change quantity or medication to a higher-cost equivalent
What this looks like
Document fraud in 2026 — three concrete patterns
Three real fraud mechanics we catch at the structural PDF layer.
Repair estimate: add parts that were not replaced
Medical report: change amounts or details after issuance
Receipt: inflate labor hours or unit prices
Expert assessment: change the damage severity rating
Police report: alter the accident description or vehicle details
Prescription: change quantity or medication to a higher-cost equivalent
The detection gap
KYC platforms check the document. HTPBE? checks the file.
Two different checks — both matter.
KYC & identity platforms
Plaid · Persona · Alloy · Jumio
- Is this a real bank statement template?
- Does the account number match the identity?
- Is the document format consistent with the issuing bank?
Detects fake documents. Does not detect edited real documents.
HTPBE? tamper detection API
Structural PDF integrity
- Was this specific PDF file modified after it was generated?
- Do metadata timestamps match the file structure?
- Were digital signatures valid at the time of signing?
What HTPBE? checks
Forensic signals analyzed in every claims document
Five layers of analysis — results in under 3 seconds
Multiple xref tables
Authentic documents from repair shops, clinics, or suppliers have one cross-reference table. A second table means content was appended after the original save — the primary structural marker of claims document tampering.
Incremental update chain
Every editing session on a PDF adds an incremental update record. HTPBE? counts the update chain length. A repair estimate or medical report with two or more incremental updates was processed by an editing tool after issuance.
Consumer tool producer mismatch
Legitimate repair estimates come from automotive management systems; medical reports from clinical document systems. A producer field showing “iLovePDF”, “PDF24”, or “Adobe Acrobat” indicates post-issuance editing.
Modification date after incident date
The PDF ModDate updates automatically when a file is edited. If the ModDate on a repair estimate is later than the stated repair date, or on a medical report later than the treatment date, the document was modified after issuance.
Signature bypass detection
Adjuster-countersigned claim forms are sometimes stripped of the signature page and re-submitted with altered content. HTPBE? detects removed digital signature blocks at “certain” confidence — the highest verdict level.
Multi-session page assembly
Complex fraud involves assembling pages from different documents or sessions into one PDF. HTPBE?’s multi-session analysis layer detects pages that originate from different rendering sessions, a strong indicator of document 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
Which document types in a claims package can be checked for tampering?
Can it detect AI-generated fake medical reports?
inconclusive because the file was created from scratch and never edited after creation. However, the producer and creator fields will reveal the generation tool, which is itself a risk signal for medical documents that should come from clinical systems.What does a "modified" verdict mean for a repair estimate?
How does it fit into existing claims management systems?
POST /v1/analyze, retrieve the result from GET /v1/result/{id}, and flag any document with a modified or inconclusive verdict for adjuster review before proceeding. The API adds under 3 seconds to the processing time. Results can be stored alongside the claim record using the returned id.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.