CleanID™: Fraud Protection That Understands the Gray Areas
Stop bad traffic before it touches your survey — without forcing false certainty
Most data quality problems start before the first question is answered. CleanID runs pre-survey, identifying known fraudsters, repeat offenders, and risky traffic before they enter your study. Think of it like antivirus for your survey: block threats early, so researchers aren’t stuck cleaning up later.
What CleanID Does
What CleanID Protects You From
CleanID blocks known harmful intruders, detects fraud early, recognizes repeat respondents, and flags low-quality traffic patterns — all with nuanced scoring instead of blunt pass/fail rules.
Detects duplicate / repeat offenders
Identifies vendor traffic that consistently produces risk
Uses risk scoring so researchers stay in control
Blocks known intrusions before field
The Best Data Cleaning Happens Before Field
Once bad traffic enters your survey, the damage is already done: wasted spend, distorted metrics, and manual clean-up. CleanID removes known threats before they waste budget — protecting feasibility and timelines.
Our Philosophy
It’s Not All Black and White — So We Don’t Force It
Most fraud tools treat every signal the same: block or allow. That feels decisive, but it creates new problems in research. CleanID is built on a different assumption: we assume the best until proven otherwise.
Our goal is to protect your data without destroying feasibility or respondent trust.
Visual: No-Doubt vs. Complex Threats
Not All Fraud Looks the Same
CleanID treats clear intrusions differently than gray-area risks — on purpose.
No-Doubt Intrusions
(Blocked immediately — pre-survey)
These are known, technical intrusions with consistent fingerprints. CleanID blocks them automatically.
Examples:
- Media Ratings Council (MRC) Invalid Traffic (IVT)
- Public & global IP blacklists
- Deep R&D into fraud communities online Common thread: Advanced technical, repeatable methods.
Complex Threats
(Evaluated with nuance — risk scored
This is where most systems break. These threats are adaptive and designed to blend in. CleanID evaluates them with context.
Examples:
- Data centers & shared infrastructure
- Misused proxies
- Click farms
- Traffic obfuscations
- Emerging technologies like AI Synthetic Respondents Common thread: Human-driven tech hacks.
How CleanID Works
Built on Signal, Not Guesswork
CleanID evaluates risk in milliseconds using a system designed to keep pace with modern fraud.
Evaluates 800+ technical respondent attributes in a fraction of a second
Compares combinations of signals against identified fraud patterns in MRX and beyond
Uses network effects + machine learning to detect emerging patterns
Incorporates respondent outcome history and blocks repeat QC offenders via ORScore
Continuously challenged by reformed hackers (white hats) to expose new vulnerabilities
Constant Improvement Loop incorporating project outcomes, and deep R&D from the dark web. (Data Point: ~20 updates per year)
Built for Researchers, Not Vendor Optics
Calibrated to Researcher Expertise
CleanID is calibrated to researcher judgment, not sample company opinions. You stay in control, with clearer signal and fewer false positives. It’s not about blocking more traffic — it’s about blocking the right traffic early.
What You Get
Clear Outputs You Can Act On
Evaluation Scoring
Nuanced scoring that reflects real-world complexity
Performance Summary
Every project receives a report showing fraud and duplicate prevention impact
Outcome
Time Back Where It Matters
On average, CleanID returns 50% of data-cleaning labor back to the researcher — less manual scrubbing, fewer surprises, and more time for insight.
Stop Cleaning Up After Fraud
Let CleanID block known threats before they reach your survey — and give your team time back where it counts.