How AI can help Risk Managers streamline processes & unlock resources for more strategic tasks

Getting internal resources to fight abuse can be a constant uphill battle no matter how large the company. It often feels like abuse teams don’t have an initial seat at the product table and as a result are forced to react to new abuse types and the manual reviews these types generate.

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In this webinar, you’ll learn:

  • What new signals can be used to detect abuse
  • How AI can be used to get a better understanding of which users you can trust
  • How to align incentives with cross functional partners to create win / win scenarios

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Kevin Lee – Panelist

Trust & Safety Architect, Sift Science

Kevin Lee is driven by building high performing teams and systems to combat malicious behavior. He has worked for the last 10+ years around developing strategies, tools and teams responsible for billions of users and dollars of revenue. Prior to Sift Science, Kevin worked as a manager at Facebook, Square and Google where he lead various risk, chargeback, spam and trust and safety organizations.

Jeff Sakasegawa – Panelist

Trust & Safety Architect, Sift Science

Jeff Sakasegawa is a Trust and Safety Architect for Sift Science. He has spent over ten years fighting fraud for Google, Facebook, and Square. In addition, his last two years at Square were spent managing teams covering BSA/AML reviews, quality assurance, identify verification, and host of other compliance related matters. He has a passion for cross functional and scalable solutions that delight as opposed to simply deliver.

Thousands of sites and apps build trust with Sift

Open Table reduced manual review by 80%. 200% improvement in detection accuracy.
60–70% reduction in spam content, more frictionless environments for valued users.
Entropay increased user conversion rates by 15% and now dedicates 0 full-time employees to fraud.