PDF Fraud Detection API — Use Cases
One API, six fraud detection workflows. Plug forensic PDF analysis into your lending, accounts payable, HR, claims, or contract management pipeline — without changing the rest of your stack.
Same API, every workflow
One endpoint behind every fraud check
Every use case below uses the same POST /v1/analyze endpoint. The verdict shape — intact, modified, or inconclusive — is identical regardless of document type. Only the named markers in the response differ.
Same key, same quota, same SDK. Bank statements, invoices, certificates, and contracts share one integration; you are not buying separate vendors per document type.
No comparison against a “clean” original or third-party database — HTPBE? reads the PDF’s internal structure and decides on its own.
Featured patterns
Six fraud patterns we catch most often
These six document types account for the bulk of submitted document fraud across lending, hiring, AP, and claims workflows. Each card lists the structural markers the engine looks for and links to the integration guide.
Bank Statement Fraud Detection
Detect Excel-edited and fabricated bank statements before they reach underwriters. 59% of fraudulent documents in lending are bank statements.
- Microsoft Excel or LibreOffice Calc as producer — not a banking system
- Multiple xref tables indicating post-export editing
- Modification date hours or days after creation date
Invoice Tamper Detection
Detect tampered invoices before payment. Catch altered amounts, payee details, and bank account numbers that look identical to the originals.
- Multiple xref tables — primary sign of post-save editing
- Incremental update chain indicating content was appended
- Producer field showing an online PDF editor instead of accounting software
Certificate Fraud Detection
Detect fake diplomas, forged credentials, and altered certificates of completion before hiring. No original document or database required.
- Consumer PDF tool as producer on an “institutional” certificate
- Modification date months or years after creation date
- Incremental updates on a document that should be immutable
Contract Tamper Detection
Detect clause changes, signature bypass, and post-signing modifications. The only verdict that reaches “certain” confidence is post-signature modification.
- Modifications after digital signature — “certain” confidence verdict
- Signature removal detected in document structure
- Execution date inconsistency with ModDate metadata
Insurance Claims Fraud Detection
Detect altered repair estimates, inflated medical reports, and fabricated receipts at document intake, before adjuster review.
- Consumer PDF editor as producer on a claims document
- Incremental updates indicating post-issuance editing
- Modification date after the stated incident or treatment date
Immigration Document Fraud Detection
Detect tampered visa documents before submission. Catch edited bank statements, forged recommendation letters, and altered Certificates of Sponsorship.
- Modification date inconsistency in a Certificate of Sponsorship
- Post-signature edits on a recommendation letter
- Excel-modified bank statement submitted as visa evidence
What ships in the box
Three properties that simplify your integration
one integrationOne key, one endpoint, every document type
Bank statements, invoices, certificates, contracts — all share the same account and quota. Add a new document type to your pipeline without procurement.
no original neededStructural analysis, not comparison
HTPBE? reads the PDF’s internal structure on its own. There is no comparison against a “clean” original or a third-party institution database to maintain.
self-serveFrom signup to first call in minutes
Plans from $15/mo, no sales call, no procurement process. Test keys are available on every plan for staging environments and CI pipelines.
By role
Fraud detection by team
Same engine, framed for the team that owns the workflow. Each page maps the detection layers to the document types your team sees.
Document coverage
Detection by document type
All 60 forensic checks run on every document type below. Each card explains the fraud patterns specific to that document and what the API returns.
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.
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
Secure your workflow
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