CryptoKiller - Top Crypto Scams Checker

Inside CryptoKiller's Methodology: How a 100-Point Scam Score Gets Built

Most "is X a scam" websites are opinion factories. CryptoKiller.org is something different — a forensic pipeline that converts raw advertising data, regulatory filings, and domain intelligence into an auditable threat score between 0 and 100. Every number on the site can be traced back to the evidence that produced it.

This is a technical walkthrough of how that pipeline actually works — the six evidence categories, the detection stack, the scoring logic, and the editorial controls that keep it defensible. If you've ever wondered why one crypto platform earns a 100/100 while another sits at 37/100, this is the answer.

The Core Premise: Scams Leave Ad Exhaust

Crypto fraud in 2026 is an advertising business before it's anything else. Before a single deposit is extracted, the operator must first buy the click — and to buy clicks at the scale needed to sustain a transnational call-center operation, they must run hundreds of ad creatives across dozens of countries through multiple ad networks.

That advertising exhaust is the evidence trail. You cannot operate a multi-million-dollar scam invisibly; you have to advertise, and advertising leaves creative assets, targeting parameters, localization patterns, and velocity curves that can be captured, cataloged, and pattern-matched.

CryptoKiller's methodology is built on this observation. The platform's internal tool — SpyOwl, its proprietary ad surveillance system — continuously scans major ad networks across 80+ countries, capturing every unique creative associated with flagged crypto brands. That captured data then feeds a six-category evidence framework.

The Six Evidence Categories

Every threat score on CryptoKiller is decomposed into six inputs. This is the published scoring framework — not a black box.

1. Ad creative volume — total unique ads captured, plus creative velocity (new ads per week)
2. Geographic targeting spread — number of countries and regions receiving localized ads
3. Celebrity impersonation — count and identity of public figures whose likeness is exploited
4. Funnel and registration patterns — landing page behavior, form design, call-center follow-up
5. Regulatory and infrastructure signals — FCA/SEC/ASIC/FINMA warnings, domain rotation, shell entity patterns
6. Historical pattern matching — behavioral similarity to 500+ previously documented campaigns

Each category contributes weighted evidence. The final 0–100 score reflects the cumulative weight, and — critically — every component is visible on the investigation page. A reader can see exactly why Trade Vector AI hit 100/100 and Valdorexa sits at 37/100.

Category 1: Ad Creative Volume and Velocity

Volume is the easiest signal but the most misread. A high ad count alone doesn't prove fraud — legitimate fintech brands also run hundreds of ads. What matters is the pattern of production.

CryptoKiller tracks two sub-metrics here:

• Total creative count over campaign lifetime — Trade Vector AI: 424 creatives over 203 days. PrimeAura: 372 creatives over 150 days.
• Creative velocity in the trailing 7 days — the pulse of the operation. A rising velocity after 90+ days signals reinvestment of stolen funds into new victim acquisition. A burst-pause-burst pattern correlates with call-center capacity cycling.

The Trade Vector AI investigation flagged a 9-creative burst following a 2-week lull — a signature CryptoKiller analysts associate with operators onboarding a new batch of phone-room staff. PrimeAura showed a different tell: a 24-hour velocity spike on March 28, 2026 where 4 creatives dropped simultaneously into Scandinavian markets, suggesting a synchronized campaign push rather than organic growth.

This is the difference between counting ads and reading ad behavior.

Category 2: Geographic Targeting Spread

Legitimate crypto platforms expand geographically in a disciplined sequence — one market, compliance review, next market. Scams do the opposite: they carpet-bomb 20+ countries from day one because the economics only work at scale.

CryptoKiller measures:

• Country count — raw breadth (PrimeAura hit 33 countries; Trade Vector AI, 19)
• Continent spread — a 6-continent footprint signals industrial infrastructure, not a startup marketing budget
• Localization sophistication — are creatives translated into 8+ languages, with country-specific celebrity swaps? Rubby Pérez appears only in Dominican Republic ads. Peter Obi videos target only Nigeria. That level of tailoring requires translation teams and celebrity research teams — overhead only a real fraud operation can justify.

The signal isn't "they advertise abroad." It's "they advertise in Kuwait, Nigeria, Hungary, and Peru simultaneously, each with a different native-language celebrity, from day one." That pattern does not exist in legitimate fintech.

Category 3: Celebrity Impersonation

This is the most visible red flag and the one CryptoKiller quantifies most precisely. Each investigation documents:

• Total distinct public figures impersonated (PrimeAura: 127. Trade Vector AI: 81.)
• Reuse rate per celebrity — Wallace Chung appeared in 7 separate Hong Kong creatives for Trade Vector AI, the highest single-celebrity reuse in that campaign and a signal that the operator tested variants and found one that converted
• Politically exposed person (PEP) count — 22 of PrimeAura's 127 impersonated figures hold or recently held government office. Exploiting sitting heads of state is a distinct sub-tactic designed to manufacture governmental legitimacy
• Format mix — static image vs. video deepfake. Video deepfakes are 3x more persuasive than static ads. PrimeAura used video in 5 of 8 sampled creatives, well above the 30% baseline across CryptoKiller's 500+ campaign database

Celebrity identification itself is performed via facial recognition matching against a known-public-figure database, then manually verified by human reviewers. The automation narrows the candidate set; the human confirms identity. This two-stage design prevents the false-positives that plague pure-automation systems.

Category 4: Funnel and Registration Patterns

Every tracked scam follows the same four-stage playbook:

1. Celebrity ad → click
2. Registration form → name, email, phone
3. Call-center outreach → "senior account manager" pitches a minimum deposit ($250 / €250)
4. Fake dashboard showing 300–500% gains → pressure to deposit more → withdrawal trap

CryptoKiller analysts document each stage with captured assets: landing page URLs, form field composition, call scripts (where obtainable from victim reports), and the specific fee-extraction language used in the withdrawal trap ("tax penalty," "identity verification fee," "compliance deposit"). When the same funnel appears across multiple brands with cosmetic changes, that's the fingerprint of a shared operator.

Category 5: Regulatory and Infrastructure Signals

This is the category that moves a score toward 100. CryptoKiller cross-references every investigated brand against four primary regulatory databases:

• FCA Warning List (United Kingdom)
• ASIC Investor Alert List (Australia)
• SEC EDGAR (United States)
• FINMA Warning List (Switzerland)

Three outcomes are possible:

• Active warning named — the highest-weight signal. The FCA naming trade-vectorai.net specifically is what pushed Trade Vector AI to 100/100.
• Zero registrations across all four — operating across 33 countries without a single license (PrimeAura) is itself evidence of unlicensed operation.
• Registration exists under a similar name — triggers an editorial warning on the review page clarifying the scope, because some scams rent the brand name of a legitimate entity.

Infrastructure signals run in parallel: domain rotation (PrimeAura cycled through 3 TLDs over 150 days), geographic domain variants (Trade Vector AI ran trade-vectorai.net alongside tradevectoraikw.com for Kuwait targeting), and creative resubmission under altered campaign IDs — a technique used to bypass platform ad review after a flagged creative gets pulled.

Category 6: Historical Pattern Matching

This is CryptoKiller's structural advantage. With 500+ previously documented campaigns feeding an internal pattern library, a new brand is never analyzed in isolation — it's scored against everything that came before.

Pattern matching surfaces behavioral fingerprints that single-review platforms miss entirely:

• The burst-pause-burst creative velocity associated with call-center shift rotation
• The West African deepfake localization pattern (global figure + sitting head of state) documented across 23+ campaigns
• The Gulf Cooperation Council domain-variant pattern (base domain + country-code suffix)
• The PEP-heavy impersonation roster associated with specific operator clusters

When a new brand matches three or more established patterns, the score weighting accelerates. This is how CryptoKiller can publish a defensible high-confidence verdict within days of a campaign emerging — the patterns are pre-validated.

The Editorial Pipeline: From Detection to Publication

The technical stack feeds a structured four-stage editorial workflow:

1. Automated detection — SpyOwl flags anomalous ad clusters
2. Evidence collection — creatives cataloged by geography, celebrity, format, and landing URL; screenshots preserved with cryptographic timestamps for evidentiary integrity
3. Analysis and scoring — six-category framework applied, pattern matching executed against the 500+ campaign database
4. Human editorial review — every investigation is reviewed by a human before publication

That final human step is non-negotiable. It's what allows CryptoKiller to publish reviews that carry explicit scope warnings — "this analysis covers trade-vectorai.net and tradevectoraikw.com specifically; if you hold a confirmed FCA-registered account with a different entity, this review does not describe your provider" — rather than the blanket accusations that sink most scam-review sites into defamation exposure.

Why the 0–100 Scale Works

A binary "scam / not scam" label is too blunt for reality. The 0–100 scale captures gradations that matter operationally:

• 100/100 (Trade Vector AI, Quantum AI) — active regulatory warning + industrial ad scale + surging velocity. Full evidentiary confirmation.
• 96–98/100 (Senvix, PrimeAura) — overwhelming behavioral evidence but no named regulatory action yet. Not-yet-warned is not the same as safe.
• 49–65/100 (Quarix AI, Floventra) — confirmed fraud pattern but smaller scale or shorter operational window.
• Below 40/100 — early-stage or low-activity operations where evidence is present but volume-limited.

Critically, a lower score does not mean "safer to use." Every published investigation concludes with the same verdict family: avoid, do not deposit, disengage. The score measures evidentiary strength, not risk tolerance.

The Defensibility Layer

Publishing accusations about fraud operations is legally hazardous. CryptoKiller's methodology is structured to survive legal challenge in three ways:

1. Evidence-first, not opinion-first. Every claim on every investigation page links to captured ad creatives, domain records, or regulatory filings. Opinion is minimized; documentation dominates.
2. Scope-specific naming. Reviews name domains, not just brand strings. "Trade Vector AI" as a generic phrase could collide with legitimate businesses; trade-vectorai.net is a specific, identifiable entity.
3. Editorial corrections channel. A published email address ([email protected]) gives any named entity a direct route to challenge factual errors. This creates a record of good-faith correctability — a defensive shield against malice claims.

Combined with the named operating entity (DEX Algo Technologies Pte Ltd., Singapore) and the stated refusal of pay-to-remove arrangements, this is a methodology built to stand up in court, not just on the internet.

The Takeaway

CryptoKiller's threat score is not a judgment — it's the output of a pipeline. Ad surveillance captures the raw evidence. A six-category framework weights it. A 500+ campaign pattern library contextualizes it. Human editorial review validates it. And every input that produced the final number is published on the page.

That is what distinguishes intelligence from opinion. In a vertical where reviews are routinely bought, traded, and weaponized, a methodology that shows its work is the only kind worth trusting.