An Investigation Into the Scam-Detector Industry, Its Power — and Its Blind Spots
This article examines how a small number of highly visible “scam detection” platforms have acquired significant reputational power over websites worldwide — and why that power raises legitimate questions about transparency, accountability, and due process.
All statements below are based on publicly available information.
Where conclusions are drawn, they are explicitly marked as analysis or opinion.
Entities mentioned are invited to respond publicly.
The Claim: Protecting Consumers Through Trust Scores
Platforms such as ScamAdviser and Scam-Detector present themselves as consumer-protection services. They promise to help users determine whether a website is “legitimate” or “possibly a scam,” typically by assigning a numerical trust score generated by automated analysis.
ScamAdviser states that it:
helps millions of users per month
uses 40+ data sources
continuously improves its algorithm
allows businesses to access its data via API
Scam-Detector presents its service using institutional language and, in some materials, references the Government Technology Agency (“GovTech”).
On the surface, this suggests authority, neutrality, and public interest.
The operational reality deserves closer examination.
Corporate Structures and Disclosures (Factual Overview)
ScamAdviser
According to its own disclosures, ScamAdviser operates under:
Ecommerce Operations B.V.
Amsterdam, The Netherlands
Registered company, VAT number provided, Chamber of Commerce listed.
This establishes a clear legal entity under EU corporate law.
Scam-Detector
Scam-Detector identifies its operational entity as:
Scam Detector Media
151 Calle de San Francisco
Suite 200
San Juan, PR 00901
Puerto Rico
Based on publicly accessible materials at the time of writing:
No tax identification number is publicly listed
No company registration number is disclosed
No legal form (LLC, Corp, etc.) is specified
No responsible officer or director is named
The address corresponds to a commercial office / serviced office location.
Such arrangements are legal and common — but they provide limited insight into operational substance.
These are observations, not allegations.
The Case Study: snuptoo.online
The website snuptoo.online was reviewed by multiple reputation platforms within a short time frame, all using automated methods.
No platform alleged criminal activity.
No victims were identified.
No financial transactions were analyzed.
Results
Scam-Detector:
Score ~22/100, language: “not recommended,” “high-risk,” “what to do if you lost money”ScamAdviser:
Language: “this site might be a scam,” “low trust score”Gridinsoft:
Score 68/100, conclusion: “legitimate,” “safe to use,” “trusted but verify”
All three relied on similar inputs:
young domain age
WHOIS privacy
limited traffic
valid SSL certificate
Same data.
Different verdicts.
What This Demonstrates (Analytical Conclusion)
These results demonstrate that trust scores are not objective measurements, but interpretive outputs.
The decisive variable is not security evidence — it is:
language framing
risk tolerance
business philosophy
One model treats uncertainty as danger.
Another treats uncertainty as context.
Neither approach is illegal.
But only one avoids reputational harm without proof.
Language as Power
Phrases such as:
“Is this legit or a scam?”
“We do not recommend this site”
“What to do if you already lost money”
do not state facts.
They prime conclusions.
From a legal and journalistic perspective, this is implicative language:
it suggests harm without asserting it.
When indexed by search engines, such language can cause real reputational and economic damage — without meeting evidentiary standards.
Monetization and Structural Incentives
ScamAdviser openly states that:
businesses may access its data via API
trust and verification services are offered
Scam-Detector promotes “most trusted partners” alongside warnings.
This creates a structural incentive problem:
The same systems that generate reputational risk also offer paid mechanisms associated with trust, visibility, or remediation.
This is not proof of wrongdoing.
It is a conflict-of-interest risk that deserves disclosure and scrutiny.
Transparency Standards — Applied Asymmetrically
Both ScamAdviser and Scam-Detector routinely cite the following as risk indicators for third-party websites:
WHOIS privacy
unclear ownership
limited corporate disclosure
short operational history
At the same time:
ownership and responsibility structures of the platforms themselves are limited or opaque
no independent appeals process is disclosed
no neutral adjudication mechanism exists
This asymmetry weakens credibility.
A platform that judges others must meet equal or higher transparency standards.
Government Framing Without Government Process
Scam-Detector’s references to GovTech create an impression of public authority.
However:
no judicial mandate is cited
no statutory authority is referenced
no formal due-process safeguards are described
If a service is governmental, it must adhere to public-law standards.
If it is private, government-adjacent framing must be used with extreme care.
Clarification is necessary.
Open Questions (Invitation to Respond)
This investigation raises legitimate, answerable questions:
How are trust scores weighted, and why are criteria undisclosed?
Why does identical data lead to contradictory conclusions?
How are conflicts of interest mitigated?
Why is fear-based language used without evidence of harm?
What formal appeal mechanisms exist for affected websites?
ScamAdviser and Scam Detector Media are invited to respond publicly.
Conclusion: Authority Requires Accountability
Scam detection matters.
Consumer protection matters.
But systems that:
conflate newness with danger
replace evidence with implication
monetize uncertainty
avoid reciprocal transparency
do not strengthen trust.
They centralize it.
In open digital societies, reputational authority must be constrained by accountability, proportionality, and due process.
Anything less is not safety.
It is risk.
