Data Integrity & Fraud Prevention

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Wednesday, Sep 16th at 12:00 PM ET

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Introductory Presentation by Greenbook + Tech Demos with Live Q&A

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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.

Join us Wednesday, September 16th to see new innovations in data integrity and fraud-prevention technology!

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

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

Greenbook's Guide to Data Integrity & Fraud Prevention Technology

Class

presented on September 16, 2026

Data integrity & fraud prevention technology overview
Types of solutions
Key features
Next frontiers

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Quest Mindshare: Glass-Box Fraud Prevention That Reveals the Signals Behind Sample Quality

Demo

presented on September 16, 2026

Other platforms give you a number. We give you three layers of why.

Most fraud-detection tools return a risk score, but it's hard to gauge severity or know how to interpret the number. In this 20-minute showcase, see how Quest's integrated data integrity approach, powered by dtect, replaces that guesswork with three layers of insight: a clear good/bad/suspicious read, the specific flags behind it (VPN usage, automation, device mismatch), and the granular detail underneath, timezone, location, and IP inconsistencies, and more.

Through a real-world use case, we'll show Quest and dtect catching duplicate attempts, network masking, bot activity, device manipulation, AI-assisted fraud, and behavioral anomalies, all evaluated before a respondent enters the survey, with the full reasoning exposed at every layer.

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The Logit Group: Beyond Bots - The New Fraud Patterns Undermining Research Data

Demo

presented on September 16, 2026

Research fraud is becoming more sophisticated, and many of the signals are no longer obvious. Bad actors can appear human, provide believable answers, and pass traditional quality checks while still compromising the integrity of a study. In this session, Steve Male will examine the different types of fraud and poor-quality behaviour being identified across online research, using real-world patterns observed through Calibr8. The discussion will explore issues such as identity manipulation, location masking, AI-generated responses, inconsistent behaviour, low engagement, and respondents who look legitimate when evaluated through only a single quality signal. Attendees will learn why fraud detection requires a layered approach, how different indicators work together, and what research teams should consider when designing a modern data-integrity strategy.

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Map of the Landscape

See data integrity and fraud-prevention technology as a set of distinct solution types addressing different points of exposure, and leave with a framework for evaluating the differences.

The Features that Matter Most

Understand which capabilities are genuine differentiators, and where AI is changing detection and where it isn’t.

What to Ask Vendors

Leave with a clearer sense of your own requirements and the questions that separate genuine capability from a polished demo.