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Spotting the Invisible: Cultural Considerations in Fraud Prevention

About

In today’s global research landscape, understanding cultural context is crucial for developing effective fraud detection strategies. Screening respondents based on certain device settings or behaviors always carries the risk of biasing the remaining sample – especially when conducting quality checks at scale.

Join quality experts Marie Hense, global head of quality at Toluna, and Leib Litman, chief research officer at CloudResearch, for an insightful webinar packed with learnings from their extensive experience in global fraud detection. They will share real-life data on behavioral and device fraud checks, highlighting how cultural nuances can significantly impact detection effectiveness, introduce bias and create a trade-off between efficient and effective quality checks.

By attending, you’ll discover why a tailored approach to fraud detection is necessary and gain actionable insights to enhance your data-cleaning procedures, ensuring effective and accurate quality checks.

Featured Presenters
Webinar hosting presenter

Marie Hense

global head of quality, Toluna
Marie is a seasoned research professional with over 10 years of experience in research, data and insights roles across various countries, including the U.K., Germany, Spain and China. With a background that spans client, consultancy and agency settings, she possesses a holistic understanding of research processes from inception to the business application of findings. As the global head of quality at Toluna, Marie leads a team of subject-matter experts in research methodology, supply and technology to prevent and address quality challenges across Toluna’s global business.
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Webinar hosting presenter

Leib Litman

chief research officer, CloudResearch
Leib Litman is co-founder and chief research officer at CloudResearch and is professor of psychology at Lander College. Leib received his Ph.D. in experimental psychology and was a research scientist in cognitive neuroscience at NYU. Leib’s current work focuses on the development and validation of scalable tools for the acquisition of high-quality data on online platforms.
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