Fighting fraud at the source: how AI-moderated interviews are reshaping data quality in research 

Fraud in market research has moved far beyond gibberish text and lazy speeders. Today, the threats are systemic, sophisticated, and often AI-powered. Bots, professional respondents, and even genuine participants outsourcing answers to ChatGPT are reshaping what “bad data” looks like.

At Glaut, we’ve just published a new whitepaper that maps the full spectrum of fraud cases, edge behaviors, and systemic risks, together with the agents AI-moderated interviews use to address them in real time.

What you’ll learn inside

This fraud prevention paper is a researcher’s guide: clear, evidence-led, and grounded in practical safeguards.

• The evolution of fraud: how today’s threats include AI-generated responses, suspiciously polished answers, and uncooperative behaviors that slip past legacy defenses.
• Systemic incentives: why compensation structures, payment forms, and panel design fuel fraud in both B2C and B2B contexts.
• Built-in defenses: how AIMIs (AI-Moderated Interviews) prevent fraud during data collection, not just after, through mechanisms such as: Voice-Only Mode, Uncooperative Detector, Consistency Evaluation, Interpretative Scoring, and Copy-Paste Blocking.‍

Download the full paper

Fraud isn’t going away. But researchers now have tools that protect data while it is being created—not just cleaned afterward.

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Looking forward

Researchers are already using Glaut to produce insights 20x faster and 3x cheaper, in 50+ languages, with full transparency and accountability. We see AIMIs not only as a safeguard, but as part of a broader evolution in research methods. As more organizations adopt AI-native approaches, the focus shifts from catching fraud at the margins to designing it out from the start.

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