Data Integrity & Fraud Prevention

The tells you relied on are eroding. A thin or incoherent open-end used to mark a fraud; now a language model writes a clean, on-topic verbatim in seconds. Completion times look human. Fingerprints come off real devices on residential IPs. Generative AI has made fraudulent respondents cheap to produce at scale and steadily harder to separate from the panelists you actually want.

The toolset answering this has multiplied fast: device and network forensics, behavioral biometrics that read how a session is taken, content models that score the open-ends, cross-study reputation systems that recognize the same person across panels — and, increasingly, checks at the incentive payout, where professional respondents cash out. Almost every panel and platform now ships its own score or shield.

What each one actually catches, where they overlap, and what slips through may not live up to the rhetoric, and vendors might describe their detection better than they can your exposure. Video and voice work opens another front, where deepfakes and impersonation reach into qualitative research.

Learn what distinguishes the solution types, when to use each, and how technology innovators are keeping pace with 

Who should attend?

• Sample, panel, and data-quality leads
• Research operations and data-collection teams
•  Quantitative and survey research teams
• Qualitative researchers running video and voice interviews
• Insights leaders accountable for the data behind decisions
• Research and insights buyers at any stage of evaluation