Choosing a document verification partner is a significant decision. This page helps procurement and operations teams understand how TamperCheck compares to Snappt - on pricing, privacy posture, and what it actually takes to get started. Note that TamperCheck works on digital PDFs and scanned hard copies alike - including physical stamp verification and handwritten signature analysis, not just native digital files.
Digital PDFs
text layer + raster analysis
Phone photos & scans
camera captures and flatbed scans
Physical stamp verification
ink bleed, embossing, overlay detection
Handwritten signature analysis
forgery and substitution checks
Documents covered
- TamperCheck.ai
- 100+ document types from 190+ countries
- Snappt
- 12 categories - pay stubs, bank statements, tax transcripts, utility bills, benefit letters - from 2,000+ financial institutions (their claim)
Forensic checks per document
- TamperCheck.ai
- 200+ forensic layers on every upload
- Snappt
- 10,000+ document features; AI trained on 16M+ documents (their claim)
AI-generated documents
- TamperCheck.ai
- Dedicated synthesis pass, resolved to the field
- Snappt
- No dedicated synthesis pass published - a forensics team updates models, and payroll connections are offered as a route around the document
Image forgery detection
- TamperCheck.ai
- Pixel-level: splice, resave and edge-sharpness deltas
- Snappt
- Not published as a separate layer - the method is described as metadata and feature checks
PDF & file structure
- TamperCheck.ai
- Objects, fonts, metadata and editor traces
- Snappt
- Yes - 'thousands of metadata elements' (their claim)
Malware & file safety scanning
- TamperCheck.ai
- On every upload
- Snappt
- Not advertised
Digital signature certificates
- TamperCheck.ai
- Issuer and trust chain verified
- Snappt
- Not publishedLimited
Field-level alteration
- TamperCheck.ai
- Yes - the altered field named and located
- Snappt
- A pass/fail recommendation per document
Scans, photos & physical marks
- TamperCheck.ai
- Stamps, embossing, signatures, print artefacts
- Snappt
- Applicant uploads inside the leasing flow
Automated QC on findings
- TamperCheck.ai
- Every finding re-validated before you see it
- Snappt
- “Fraud Analyst Review” listed as a feature; per-finding QC not published
What you get back
- TamperCheck.ai
- A verdict, the field named, in plain English
- Snappt
- A pass/fail recommendation in the property-management dashboard
Time to verdict
- TamperCheck.ai
- About a minute, unattended
- Snappt
- 10 minutes or less (their claim)
Price per document
- TamperCheck.ai
- $0.50, published
- Snappt
- Not published - Standard, Professional and Enterprise bundles, quoted
Commitment
- TamperCheck.ai
- None - no seat fees, no minimum, no cap
- Snappt
- Bundled leasing contract; Synthetic Identity Detection only on Professional and Enterprise
How you start
- TamperCheck.ai
- Self-serve - first top-up matched free, live the same day
- Snappt
- Talk to sales
Document retention
- TamperCheck.ai
- Zero - analysed in memory, never written to disk
- Snappt
- “No longer than is reasonably necessary” - no fixed period published
Training on your documents
- TamperCheck.ai
- Never
- Snappt
- AI trained on 16M+ documents; policy lets de-identified data be used and shared “without limitation”
| TamperCheck.ai | Snappt | |
|---|---|---|
| Detection depth | ||
| Documents covered | 100+ document types from 190+ countries | 12 categories - pay stubs, bank statements, tax transcripts, utility bills, benefit letters - from 2,000+ financial institutions (their claim) |
| Forensic checks per document | 200+ forensic layers on every upload | 10,000+ document features; AI trained on 16M+ documents (their claim) |
| AI-generated documents | Dedicated synthesis pass, resolved to the field | No dedicated synthesis pass published - a forensics team updates models, and payroll connections are offered as a route around the document |
| Image forgery detection | Pixel-level: splice, resave and edge-sharpness deltas | Not published as a separate layer - the method is described as metadata and feature checks |
| PDF & file structure | Objects, fonts, metadata and editor traces | Yes - 'thousands of metadata elements' (their claim) |
| Malware & file safety scanning | On every upload | Not advertised |
| Digital signature certificates | Issuer and trust chain verified | Not publishedLimited |
| Field-level alteration | Yes - the altered field named and located | A pass/fail recommendation per document |
| Scans, photos & physical marks | Stamps, embossing, signatures, print artefacts | Applicant uploads inside the leasing flow |
| Automated QC on findings | Every finding re-validated before you see it | “Fraud Analyst Review” listed as a feature; per-finding QC not published |
| What you get back | A verdict, the field named, in plain English | A pass/fail recommendation in the property-management dashboard |
| Speed & cost | ||
| Time to verdict | About a minute, unattended | 10 minutes or less (their claim) |
| Price per document | $0.50, published | Not published - Standard, Professional and Enterprise bundles, quoted |
| Commitment | None - no seat fees, no minimum, no cap | Bundled leasing contract; Synthetic Identity Detection only on Professional and Enterprise |
| How you start | Self-serve - first top-up matched free, live the same day | Talk to sales |
| Data handling | ||
| Document retention | Zero - analysed in memory, never written to disk | “No longer than is reasonably necessary” - no fixed period published |
| Training on your documents | Never | AI trained on 16M+ documents; policy lets de-identified data be used and shared “without limitation” |
When to choose TamperCheck
- The fakes getting through were never edited. Snappt scans metadata and flags changes "as edited." A pay stub generated end to end has no change to flag - its metadata was written once, its layout can imitate a payroll provider already in the database. Catching it takes per-field rendering forensics and a dedicated synthesis pass, not a check that looks for evidence of editing.
- Your applicants include the ones who can't connect payroll. Snappt's answer to generated documents is payroll connectivity. Works for a salaried applicant at a connected employer. Gig workers, the self-employed, new arrivals, anyone paid by a foreign employer still uploads a document - that's the path a generated fake arrives on.
- You screen more than US rental applicants. Lending, insurance, hiring, marketplaces, supplier onboarding - and a Nigerian payslip, UAE trade licence or Brazilian tax notice. TamperCheck covers 100+ document types from 190+ countries with no institution database to enrol in.
- Your team has to explain a denial. "Net pay doesn't equal gross minus deductions, and that figure is in a different font weight" is a reason you can defend. A pass/fail recommendation is a result, not a reason.
- You want every check at a published price. TamperCheck is $0.50 per document with AI-generation detection included on every upload - not a quoted bundle where synthetic-identity detection is reserved for upper tiers.
- Digital signatures are verified, not just displayed. When a document carries one, we resolve the certificate itself: who issued it, whether that issuer is on a trusted list, and whether the chain is complete.
When to choose Snappt
- You're a US multifamily operator buying a leasing workflow, not a detector. Snappt bundles income, identity and rent verification with an applicant experience and connectors for Yardi, RealPage, Entrata and ResMan. Real convenience - the fraud check inside it is still metadata-led, so the question of what catches a generated pay stub remains yours.
- Most of your applicants are salaried and can connect payroll. For them, pulling income straight from the source sidesteps the document entirely. The applicants who can't connect are the ones still uploading a pay stub - exactly the population a document forensics engine exists for.
- You want income, ID and rent-history from one vendor. We verify documents only, by design. If one leasing contract matters more than depth, Snappt sells more of the application layer.
A leasing platform's fraud check against documents that were never edited
Start with what Snappt is. Its pricing page sells bundles of income verification - Standard, Professional, Enterprise - with an applicant experience and add-ons for rent verification and synthetic identity. Document fraud detection is one component, integrated into the property-management systems leasing offices already run. The unit of work is an applicant for a US lease. TamperCheck answers the narrower, deeper question underneath that workflow, for any industry and any country: is this document real, and if not, which field changed?
Snappt's detection method is metadata and known-institution matching. Their product page describes analysing "10,000+ document features against our database of 2,000+ financial institutions"; their own blog explains that because the system "scans all of the metadata," an edit is caught and flagged "as edited." Against someone who opens a real bank statement and retypes a balance, that works. The threat model has moved. A pay stub generated end to end was never edited, carries metadata written once, and can be produced in the layout of a payroll provider already in the database. No edit for an edit-detector to find.
Snappt's own writing names "AI-generated pay stubs" as a threat. Their published responses: a Fraud Forensics team that updates models, and payroll connectivity - verifying income from the source instead of the document. The first is signature-style defence that learns a generator after it's been used. The second only reaches applicants whose income sits in a connectable payroll system. Everyone else is back to uploading a file. TamperCheck is built for that file. Each OCR field is compared against the rest of its own page - font weight, kerning, glyph rendering, edge sharpness, recompression history - so a generated document has to be internally flawless, with no genuine page to copy from. A separate AI-generation pass runs on every upload, not as an add-on.
The 99.8% figure deserves a closer look. Snappt states it as the share of fraudulent documents its technology catches, but doesn't say how many were generated rather than edited. Those are two very different detection problems. A tool tuned on years of edited statements can post an excellent number on edited statements. What matters now is the class it wasn't built around.
Output and speed. Snappt returns pass/fail to the leasing dashboard in "10 minutes or less." TamperCheck returns a verdict within seconds, unattended, with the altered field named, located and explained in plain English - every finding already re-validated by a separate QC pass before anyone sees it. A result a leasing agent can act on is useful. A reason they can repeat to an applicant, a regional manager or a fair-housing reviewer is better.
Finally, what happens to the document. Snappt's privacy policy commits to keeping personal information "no longer than reasonably necessary," publishes no fixed period, and allows de-identified information to be used and shared "without limitation"; their model is trained on 16M+ documents. TamperCheck analyses each document in memory, never writes it to disk, never trains on it. Pricing follows the same pattern: Snappt quotes bundles after a sales call, with synthetic identity detection reserved for upper tiers. TamperCheck publishes $0.50 per document, every check included, starting the same day.
TamperCheck vs Snappt: common questions
What is the best Snappt alternative?
TamperCheck.ai, especially if AI-generated documents are the worry or you screen beyond US rental applicants. Snappt scans metadata against 2,000+ institutions and returns pass/fail. TamperCheck compares every field against its own page, runs a dedicated AI-generation pass, names the altered field, covers 100+ document types from 190+ countries at $0.50 per document.
Can Snappt detect AI-generated pay stubs?
Snappt's own blog names them as a threat. Their published responses are a forensics team updating models and payroll connectivity that bypasses the document. They don't publish a dedicated AI-generation detection pass. Their method - scanning metadata and flagging edits - relies on evidence of editing, which a document generated from scratch doesn't contain.
How much does Snappt cost compared to TamperCheck?
Snappt doesn't publish prices. Standard, Professional and Enterprise bundles are quoted through sales, and synthetic identity detection is an add-on only on Professional and Enterprise. TamperCheck publishes $0.50 per document - no subscription, no minimum, no seat fees - and every check, including AI-generation detection, runs on every document.
Does TamperCheck detect fake pay stubs and bank statements?
Yes - they're among the most common documents it analyses. Each field's rendering is compared against the rest of the page to catch edited figures. Arithmetic and cross-field checks run too - net pay against gross minus deductions. And a dedicated AI-generation pass catches pay stubs fabricated wholesale rather than altered.
Can TamperCheck screen international or self-employed applicants?
Yes. 100+ document types across 190+ countries, no institution database, no per-country template setup. The analysis reads the file's own internal consistency. A foreign payslip, a self-employed invoice or a gig-platform earnings statement gets the same analysis as a US pay stub - which matters most for applicants who can't connect a payroll account.
Is TamperCheck faster than Snappt?
Typically, yes. Snappt says 10 minutes or less. TamperCheck returns a verdict within seconds, unattended, with each finding already re-validated by an automated QC pass and the altered field named in plain English.
Does Snappt keep or train on applicant documents?
Snappt's privacy policy says personal information is kept "no longer than reasonably necessary" with no fixed period, and permits de-identified information to be used and shared without limitation. Their detection model is trained on 16M+ documents. TamperCheck analyses documents in memory, never writes them to disk, never trains on them.
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