Loan Origination Document Fraud Detection — the structural-PDF layer alongside Plaid, Persona, and OFAC
HTPBE? sits in the loan-origination pipeline next to bank-data aggregation (Plaid, Tink, Bridge), identity proofing (Persona, Onfido, Alloy), and sanctions screening (OFAC, ComplyAdvantage). It is the layer that opens the binary structure of every bank statement, payslip, tax return, and asset letter the borrower uploaded — and surfaces edits invisible to OCR, visual review, and downstream data-fraud-detection. Built for alt-lenders, fintech underwriting, BNPL fraud teams, and consumer-credit risk leads who carry post-disbursement loss and repurchase exposure. For first-lien mortgage origination — LOS/POS integration, repurchase exposure on agency loans — see the Mortgage page. From $15/mo. No sales call.
The problem
Modern document fraud is invisible to visual review
A growing class of document fraud opens a genuine PDF, edits a balance, a date, or a beneficiary, and re-saves it. Visually nothing changes — the document passes pixel-level review, layout review, and KYC.
Structural PDF analysis reads the layers rendering engines never expose: revision history, object structure, signature coverage maps. That is where edits leave fingerprints they cannot wipe.
Common tampering patterns
- Modified balances or totals after export
- Swapped IBAN or beneficiary on invoices
- Post-signature edits on contracts
- Backdated issue and modification dates
- Fabricated documents from consumer PDF tools
What this looks like
What KYC stops at, and what tampered bank statements still get through
Three real fraud mechanics we catch at the structural PDF layer.
Inflated income on payslips and salary letters
The most common income fraud: a real payslip is opened in a consumer PDF editor and the salary figures are changed. The producer field shifts from the payroll software (ADP, PayFit, Sage, Xero) to the editor. The API returns KNOWN_EDITOR_IN_PRODUCER as a high-confidence marker.
Modified bank statements with edited balances
Bank statements rebuilt in Excel and exported to PDF carry spreadsheet software as the producer instead of the issuing bank’s reporting system. Edited real statements show timestamp gaps and additional revision layers. The API exposes both patterns from a single POST call.
Falsified tax returns and assessment notices
Self-employed income proof is often forged via altered tax returns or notice-of-assessment documents. The API detects modifications-after-signing on signed tax documents and producer mismatches on rebuilt forms — evidence visual review and OCR cannot find.
Fake employment fraud detection and asset letters
Employment verification letters and proof-of-asset letters are usually issued on institutional letterhead via a corporate PDF generator. When a fraudster recreates them in Word or a consumer editor, the producer fingerprint reveals the substitution. The API maintains a database of hundreds of known PDF tools to make this distinction precise.
Where the loss lands
Underwriting decisions you cannot reverse after fund
A tampered bank statement that passes intake becomes a charge-off you carry on the book.
Plaid, Tink, and Bridge confirm bank-account ownership when the borrower agrees to connect — many will not. Persona, Onfido, and Alloy verify the person, not the paperwork. OFAC and ComplyAdvantage screen names against sanctions lists, not document integrity. None of them open the PDF the borrower uploaded. Once the loan funds, post-disbursement loss is yours to absorb — and on warehouse-line or institutional capital, repurchase exposure compounds it. The structural-PDF check at intake is the cheapest place to catch the edit.
What HTPBE? checks
Detection capabilities
Deterministic structural signals. No probabilistic scores, no model training.
Producer signature mismatch
The PDF claims to come from one tool but the binary structure points to another. The first signal of post-export editing.
Incremental update trail
Every save after the original creates an incremental update. Long chains mean multiple editing sessions on the same file.
Multiple xref tables
Each editing session adds a new cross-reference table. Genuine institutional PDFs have one. Tampered PDFs have several.
Modification timestamp gap
A real PDF has matching CreationDate and ModDate. Months between them is a high-confidence forgery signal.
Digital signature validation
When a digital signature exists, we verify the coverage map. Modifications after signing return certain-confidence verdicts.
Font and object consistency
Edited text introduces new font subsets or objects with origin patterns inconsistent with the rest of the document.
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 lending documents does the API support?
How does this fit alongside Plaid and KYC platforms?
Does the API replace manual underwriter review?
What is the typical false-positive rate on lending documents?
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.