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5 Reasons Your Fraud Detection is Plummeting
DataVisor’s Yongxin Xi, Product Expert in Fraud and Risk Solutions for Banking, and Qing Han, Data Science Manager, have an action packed agenda to uncover the 5 critical root causes to understand why your fraud model isn’t working in production. You’ll learn:
- 5 areas to investigate to understand why your model isn’t working as intended
- Best practices to diagnose, evaluate, validate and optimize fraud models
- Ask the experts about critical use cases you face in your organization
Meet the Speakers
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Yongxin Xi
Dr. Yongxin Xi is an anti-fraud expert with a PhD from Princeton in machine learning theory and over a decade of experience combating spam and fraud. She has led data science and ML engineering teams in building anti-fraud solutions and now serves as a senior product consultant at DataVisor.
Qing Han
Qing has been with Datavisor since 2019, initially joining as a research scientist and later taking on a leadership role as the head of the data science team. Leveraging her anti-fraud domain knowledge and advanced machine learning expertise, she develops innovative models to provide effective fraud management solutions to clients. Prior to joining DataVisor, Qing acquired extensive engineering experience across various projects spanning multiple disciplines. This diverse background has equipped her with a broad skill set and a holistic approach to problem-solving. Qing holds a PhD degree in engineering from the Massachusetts Institute of Technology (MIT).