Original research · Reviewed August 19, 2026

Crypto bot marketing claims study

We analyzed every material claim stored in the current product dataset. The study distinguishes feature existence from outcome evidence and avoids treating company marketing as independent verification.

RT
Research byRiven Trust Research Desk

Product research, evidence review and claim verification

What did the claims review show?

The current dataset contains 45 claims across 15 platforms. 37 are Partially Verified, Unverified or Unable to Verify. That does not mean those claims are fraudulent; it means the evidence reviewed did not fully establish their material meaning.

Verification outcomes

Verified7 of 45 · 16%
Partially Verified24 of 45 · 53%
Unverified11 of 45 · 24%
Unable to Verify2 of 45 · 4%
Misleading1 of 45 · 2%

Claim categories

Security18 of 45 · 40%
Performance8 of 45 · 18%
Scale4 of 45 · 9%
Feature14 of 45 · 31%
Pricing1 of 45 · 2%

Interpretation

Feature claims

A public workflow or documentation can support that a feature exists. It does not show the feature improves returns or is suitable for a particular strategy.

Performance and AI claims

Backtests, testimonials, marketplace histories and AI labels are not independent live-return records. Verification needs a defined period, methodology, fees, drawdowns and evidence resistant to selection bias.

Scale and security claims

User counts and volume figures need definitions and independent support. Security controls can be documented while their implementation effectiveness remains unaudited.

Method

Each product record stores the original claim, claim category, source URL, evidence found, verification status and analyst note. Counts are calculated from those fields at build time. “Misleading” is reserved for research that supports a materially inaccurate impression; it is not inferred from missing evidence alone.