logo
For property managers

Fake proof-of-income is how bad tenants get keys

Fabricated pay stubs and doctored bank statements pass a visual review every day. The eviction costs more than any lease payment. Structural PDF forensics catches what the eye can’t.

~3 sec
per document
35 checks
forensic layers
From $15
per month
1,500+
docs / month on Growth
Scope

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 PDF, we’re the most specific tool for it.

How it looks

One REST call, one deterministic verdict

Upload the PDF. The API returns INTACT, MODIFIED, or INCONCLUSIVE with named markers — in about three seconds.

What this looks like

Tenant Screening document fraud in 2026

Three real fraud mechanics we catch at the structural PDF layer.

01

Fabricated pay stubs

A pay-stub generator produces a PDF with a plausible employer and inflated income. The file structure doesn’t match any real payroll system.

02

Inflated bank statement balances

Running balance edited to show padded savings or higher deposits. Incremental update markers and arithmetic checks expose the change.

03

Utility bill name swap

A legitimate utility bill with the name field replaced to establish residency fraud. Font metrics and object layout reveal the substitution.

The scale of the problem

$3,500+
average cost to evict a non-paying tenant
1 in 6
rental applications contain misrepresented financial information
~3 sec
per document via API

The verification gap

KYC platforms verify the document. HTPBE verifies the file.

Two different checks — both matter.

Credit bureaus pull reported data; they don’t examine the PDF the applicant uploaded. Income-verification platforms that rely on bank connections work only when the applicant is willing to connect — the ones who forge documents rarely are. Visual review by a leasing agent cannot detect a well-made fabrication. HTPBE operates on the file structure, independent of visual rendering.

Results in under 3 seconds30 to 1,500+ documents/monthFrom $15/mo
How it works

Five forensic layers, one deterministic verdict

Every PDF we receive passes through the same structural pipeline — no model training, no thresholds to tune.

01

Metadata analysis

Creation and modification timestamps, producer and creator fields, XMP metadata — the first layer exposes basic tampering.

02

File structure

Xref tables, trailer chain, incremental updates. Any edit after export leaves a structural fingerprint here.

03

Digital signatures

Signature chain integrity and post-signature modifications produce deterministic markers. Certainty-level signal.

04

Content integrity

Fonts, objects, embedded content, page assembly. Multi-session edits and inserted objects are visible at this layer.

05

Verdict with markers

Deterministic output: INTACT / MODIFIED / INCONCLUSIVE, with named markers for every finding — suitable for audit trail.

Document types

PDF document types we verify for tenant screening

Every type listed below is analyzed at the structural file layer — not the rendered image.

Pay stub / payslip PDFBank statement PDFEmployment verification letter PDFReference letter PDFUtility bill PDFTax return PDF
What HTPBE checks

Detection capabilities

Deterministic structural signals. No probabilistic scores, no model training.

Gross-to-net arithmetic

Pay stub math is checked against declared deductions. Generator-tool output usually fails this check.

Running balance plausibility

Statement transaction rows are validated against the running balance column — inserted credits break the sequence.

Producer field validation

Authentic payroll and bank exports have known producer signatures. Generator tools and editors leave different ones.

Text layer vs. raster mismatch

Replaced text in a rendered page causes the text and visual layers to disagree — a clean forensic signal.

Incremental update detection

Any post-export modification creates a structural trail, even when the change is a single field.

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

FAQ

Frequently asked questions

Snappt focuses on document-level classification and has a large property-manager footprint. HTPBE is a developer API with structural PDF forensics — designed to plug into rental platforms, tenant-screening services, and PropTech products rather than replace a screening workflow. If you’re building screening software, we’re the lower-level layer you integrate.

Yes. Structural analysis is format-agnostic — it reads the PDF’s internal layers regardless of the payroll system or country. Any digital PDF works.

Outside scope. A phone photo is a raster image, not a PDF with a file structure. If applicants submit photos or scanned images, the method cannot evaluate authenticity. Require digital PDF uploads for best results.

The API is stack-agnostic. Any platform that accepts uploaded PDFs and can make an outbound HTTPS call can integrate. Middleware or Zapier works; most teams wire it into their intake endpoint directly.

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.