Cleaning fraud out of a high-stakes tracker
The challenge
A financial-services tracker was being polluted by bots, duplicates and inattentive respondents. Rejection rates were high, timelines slipped on re-fielding, and confidence in the data had eroded.
Our approach
We rebuilt the quality stack around the study: digital fingerprinting, deduplication and a suppression list before the survey, plus AI-assisted open-end review, speed and straight-lining checks in-field.
The trust & data-quality layer
Screening runs in two layers — pre-survey to stop fraud at the door, in-survey to catch it mid-stream. Respondents are never told why they were removed, so the methods can't be gamed. Every rejection is logged and reported.
What this delivers
- ✓Fraud screening applied identically at every wave, so a shift in the data is a shift in the market
- ✓Far less re-fielding, so timelines held
- ✓Restored confidence for board-level reporting
More work
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