A fabricated offer letter unlocks personal loans, visas, and competing-offer leverage — visual review never catches it
Lenders see fake offer letters used to qualify for personal and pre-employment loans. Visa officers see them used to demonstrate intended employment. HR teams see them used as competing-offer leverage during salary negotiation. The pattern is the same: author in Microsoft Word using a real company template, set the salary high, sign the HR Manager’s name, export to PDF.
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 fabricated or tampered offer letter, we’re the most specific tool for it.
Offer letters are a mixed-origin document — large employers issue them through HRMS or DocuSign workflows (institutional metadata expected); small employers and startups legitimately export from Word (desktop metadata expected). When htpbe? returns INCONCLUSIVE, the meaning depends on which kind of employer is named on the letter. Read the FAQ for guidance.
One REST call, one deterministic verdict
Upload the PDF. The API returns INTACT, MODIFIED, or INCONCLUSIVE with named markers — in about three seconds.
How fake and tampered offer letters actually look
Three real fraud mechanics we catch at the structural PDF layer.
Letter fabricated in Microsoft Word from scratch
No employer involved. The candidate downloads the company logo from LinkedIn, drops it into Word, types the offer letter with an inflated salary, signs an HR Manager’s name they invented, exports to PDF. The producer field shows Microsoft Word — not the HRMS or DocuSign workflow large employers use.
Real letter with edited salary or start date
Candidate has a genuine offer but the salary doesn’t qualify them for the loan amount they want — or the start date doesn’t fit the visa timeline. They open the PDF in any editor, change the figure, re-export. Incremental update markers expose the edit.
Letter from a defunct or non-existent shell company
A "subsidiary" company name nobody can verify, with a signed offer letter at a high salary. The structural fingerprints (Word producer, single session, no e-sign chain) match a desktop fabrication regardless of whether the named company exists.
The scale
Why your existing checks miss this
BGV calls the employer. Lenders cannot.
And visual review of an offer letter PDF rarely catches a Word fabrication.
BGV platforms (AuthBridge, IDfy, OnGrid, HireRight, Sterling) call the employer to confirm an offer was made — this works for hiring contexts when the employer responds. But lenders, visa officers, and pre-onboarding HR teams often cannot make that call (no relationship with the named employer, no time, no consent path). htpbe? catches the file the candidate uploaded — standalone, no employer relationship required, no human in the loop until the structural verdict is in.
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.
Offer letter and adjacent employment-proof PDFs we check
Every type listed below is analyzed at the structural file layer — not the rendered image.
Detection capabilities
Deterministic structural signals. No probabilistic scores, no model training.
Producer signature on the offer
Authentic offer letters from large employers come from HRMS exports (Workday, SuccessFactors, BambooHR), DocuSign / Adobe Sign workflows, or in-house ATS engines. When the producer field shows Microsoft Word, LibreOffice, Google Docs, or a generic PDF library, the letter was authored on a desktop — context-dependent (small employers) but a flag against a Fortune 500 letterhead.
Digital signature presence and chain
Most large-employer offer letters are e-signed via DocuSign, Adobe Sign, or HelloSign. Authentic letters carry a valid signature chain visible in the PDF structure. Fabricated letters either lack signatures entirely or have invalidated chains — visible regardless of the visual signature image.
Incremental update trail
A clean export from an HRMS or e-sign workflow has one cross-reference table. Re-saves through any PDF editor append a second xref — visible structural evidence of post-issuance editing on a salary, start date, or signing party.
Letterhead and image-stream artefacts
Real corporate letterheads are embedded as part of the template’s font and image objects. Lifted-and-pasted logos appear as redundant image streams with mismatched compression characteristics — a structural fingerprint of fabrication.
Modification timestamp gap
A real offer issued at the time of decision has CreationDate matching ModDate (single-session export). A weeks-or-months-later modification on a "freshly issued" offer is a high-confidence flag for post-export editing.
Cross-document signature reuse
When the same hiring manager’s signature image appears across multiple "different" employer letters from a single applicant pool, image-stream hash matching exposes the shared source — a fingerprint of a single fabricator producing multiple offer letters.
Two HTTP calls to verify any offer letter
Buyers can skip this section — developers, the integration is two HTTP calls.
Step 1 — submit the PDF
curl -X POST https://api.htpbe.tech/v1/analyze \
-H "Authorization: Bearer $HTPBE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://your-storage/applicant-offer-letter.pdf"}'Step 2 — read the verdict
{
"id": "o1f2f3e4-5r6l-7t8r-9z0z-a1b2c3d4e5f6",
"status": "modified",
"modification_confidence": "high",
"modification_markers": [
"Two cross-reference tables — incremental update",
"Modification date 11 days after creation date",
"PDF editor producer detected"
],
"producer": "Adobe Acrobat Pro",
"creator": "DocuSign",
"creation_date": 1707091200,
"modification_date": 1708041600,
"has_digital_signature": false,
"xref_count": 2,
"has_incremental_updates": true
}Original came from DocuSign — institutional source. Then 11 days later it was opened in Adobe Acrobat Pro and re-saved, adding a second xref. Verdict: modified at high confidence. The candidate edited a real DocuSign offer after issuance — likely to bump the salary figure or shift the start date before submitting to a lender or visa officer.
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
Frequently asked questions
inconclusive — combine with other markers (image-stream artefacts, signature image hashes, incremental updates) before flagging. The verdict is producer-aware in context, not a binary reject.modified with the incremental-update marker even when the visual layout looks pixel-perfect. The reviewer can see the file was changed after issuance.Related solutions and guides
HR & Hiring
Pre-employment document fraud detection for talent ops and BGV operators.
Fintech & Lending
Loan applications using fabricated offer letters — fraud-ops angle for lenders.
Fake Experience Letter Detection
Sister page — same forensics applied to experience letters from previous employment.
Fake Employment Letter Detection
Same HR cluster — employment verification letter forensics for lenders and visa officers.
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