Fake Asset Letter Detection — Catch Tampered Mortgage PDFs
A gift letter is a one-page PDF that decides whether the down payment counts — and one borrowers know how to fake. Mortgage processors clear asset letters and gift letters as evidence of down-payment funds. The borrower needs the donor to confirm the money is a gift, not a loan; or the bank to confirm the balance. When the donor is reluctant — or when the balance does not actually exist — the temptation is the same: edit the letter or build one in Word with a lifted signature.
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 altered or fabricated asset / gift letter, we’re the most specific tool for it.
When htpbe? returns INCONCLUSIVE on an asset or gift letter, that’s the expected baseline (these documents legitimately come from desktop tools — a donor’s Word document or a small bank’s letter template); combine with other markers before flagging.
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 asset / gift letters actually look
Three real fraud mechanics we catch at the structural PDF layer.
Real letter edited after issuance
A genuine asset letter was issued by the bank or signed by the donor. Borrower opens it in any PDF editor, edits the balance figure or the relationship-to-borrower line, exports as PDF. The xref chain shows an incremental update — visible structural evidence of post-issuance editing.
Letter authored from scratch with lifted signature
No real letter exists. Borrower builds one in Word using bank letterhead lifted from a public source, pastes a signature image lifted from another document or social media, exports as PDF. The pasted signature image carries different JPEG/PNG compression than the surrounding document — a clean structural fingerprint of fabrication.
Same signature reused across "different" donor letters
Multiple gift letters from supposedly different donors carry visually identical signatures with identical image-stream metadata — the signature image was recycled. Cross-document image hash comparison surfaces the reuse.
The scale
Why your existing checks miss this
VOD calls the bank. The donor letter has no bank to call.
Both layers matter. The PDF the borrower uploaded is what your underwriter signs off.
Verification of Deposit services call the bank to confirm the balance — useful for asset letters but not for gift letters from individual donors. Day 1 Certainty asset verification (Account Aggregation) requires the borrower to connect the account; borrowers who fabricated the balance rarely consent. Manual donor verification is rare and slow. htpbe? catches the asset / gift letter PDF the borrower uploaded at the moment of intake — standalone, no bank-side or donor consent required.
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.
Asset, gift, and supporting mortgage 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.
Image-stream artefact detection
Lifted-and-pasted signatures and bank logos carry different JPEG/PNG compression characteristics than the surrounding document. Image-stream metadata mismatches are a structural fingerprint of fabrication or post-issuance signature swapping.
Incremental update trail
A clean letter has one cross-reference table. Edits to balance figures, dates, or signatures append a second xref — visible structural evidence of post-issuance editing.
Cross-document signature reuse
When multiple gift letters from "different" donors arrive in the same file, the API surfaces image hashes for each signature. Identical hashes across donors flag signature recycling.
Modification timestamp gap
A real letter dated last week has CreationDate ≈ ModDate within days. A months-later modification on a "freshly signed" letter is a high-confidence flag for post-issuance editing.
Producer signature analysis
Asset letters legitimately come from bank document systems or from desktop tools (small-bank letters often originate in Word). The signal is not Word-versus-bank-system; it is Word + lifted signature image + no incremental update versus Word + clean content. Combined producer + image + update markers produce the verdict.
Font subset divergence across pages
Multi-session edits leave font subset prefix shifts. Single-session legitimate letters have consistent subsets across all pages.
Two HTTP calls to verify any asset / gift 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/borrower-gift-letter.pdf"}'Step 2 — read the verdict
{
"id": "g1i2f3t4-5l6e-7t8t-9e0r-f1g2h3i4j5k6",
"status": "modified",
"modification_confidence": "high",
"modification_markers": [
"Signature image stream metadata mismatch",
"Two cross-reference tables — incremental update",
"Modification date 3 weeks after creation date"
],
"producer": "Microsoft Word",
"creator": "Microsoft Word",
"creation_date": 1706054400,
"modification_date": 1707955200,
"has_digital_signature": false,
"xref_count": 2,
"has_incremental_updates": true
}A real gift letter from a donor would typically be a single-session Word-to-PDF export with the donor’s signature embedded as a consistent image stream. Here the signature image carries different compression than the surrounding document (lifted from elsewhere), and the file was re-saved three weeks after creation (post-signing edit). Verdict: modified at high confidence. The borrower fabricated the gift letter or edited a real one after the donor signed.
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
intact or inconclusive — it has a Word producer signature but no incremental update trail and no signature image-stream artefacts. The verdict combines multiple markers; a Word producer alone is not a flag. The combination of Word producer + lifted-signature artefacts + post-creation modification is what triggers modified.Related solutions and guides
Mortgage Underwriting
Asset / gift letter + W-2 + tax return + bank statement forensics for US mortgage origination.
Fake W-2 Detection
Sister page — same forensics for the W-2 PDF that accompanies asset letters in the mortgage file.
Fake Bank Statement Detection
Bank statement forensics — the second source of asset evidence in mortgage files.
Fake 1099 Detection
Same cluster of US mortgage income docs — forensics for 1099 PDFs.
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