Detect Fake Documents — Government, Benefits & Specialised PDFs
A category hub for fake-document detection use cases that don’t fit the major document buckets — government scheme PDFs, retirement and superannuation statements, regional benefit forms. The same structural API that powers our other hubs, applied to specialised document types.
HTPBE? analyzes the structural layer of the PDF file. We do not validate the document against the issuing authority’s database (NDIS portal, ATO, individual super funds, etc.). For database-fraud detection workflows, combine HTPBE? with the issuer’s fraud detection API where one exists. HTPBE? catches the editing and fabrication layer that database lookups miss.
Government, scheme, and regional documents come from a heterogeneous set of issuers — some institutional, some via Office or letterhead templates. INCONCLUSIVE verdicts must be interpreted in the context of the specific issuer. The page-level guides below explain the expected producer fingerprints for each document type.
The problem
Specialised documents fly under the fraud radar
Visa officers, benefits administrators, lenders, insurers, and HR teams all accept specialised documents — NDIS plans, superannuation statements, benefit award letters, regional permits — as proof of eligibility, income, or status. These documents rarely have central fraud detection APIs, so reviewers fall back on visual inspection.
Visual inspection misses every form of structural fabrication. A specialised document edited in a consumer PDF tool looks identical to its source — until the binary is examined. The producer field, the cross-reference table count, the modification timestamps tell a story the rendered page hides.
Each spoke under this hub addresses a specific document type with the patterns and signals unique to that issuer. The hub aggregates the patterns; the spokes deliver the depth.
Why specialised docs are high-fraud
- Few central fraud detection databases for reviewers to query
- Long-tail issuers — no per-document AI training data
- Visual templates are easy to clone; structural origin is harder to fake
- Reviewers default to visual checks when database access is unavailable
- Fraud rings target categories with the weakest fraud detection infrastructure
What this looks like
Document fraud in 2026 — three concrete patterns
Three real fraud mechanics we catch at the structural PDF layer.
Few central fraud detection databases for reviewers to query
Long-tail issuers — no per-document AI training data
Visual templates are easy to clone; structural origin is harder to fake
Reviewers default to visual checks when database access is unavailable
Fraud rings target categories with the weakest fraud detection infrastructure
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
How structural analysis works on specialised documents
Same engine, document-type-specific interpretation
Producer fingerprint matched to known issuer profile
Each spoke under this hub maintains a profile of expected producer fingerprints for the document type. NDIS plans come from the NDIS portal export. Superannuation statements come from fund-specific reporting systems. A producer mismatch is the most reliable structural fraud signal.
Incremental update detection
A clean issuance carries one cross-reference table. Any edit appends a second xref. The marker fires regardless of document type — the meaning depends on the issuer profile.
Modification timestamp gap
Issuance date should match the embedded creation timestamp. Gaps between Issue Date / CreationDate / ModDate are high-confidence flags on documents where issuance is supposed to be a single event.
Image-stream artefacts on official seals
Government and scheme documents typically embed institutional seals or logos as part of the template. Lifted seals carry mismatched compression characteristics that structural analysis exposes immediately.
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 specialised documents does this hub cover?
How does this differ from the top-level Document Fraud Detection API?
Can I request a new specialised document type?
Are these documents region-specific?
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.