Webinar: Thursday, September 27th at 11am Eastern (New York Time)
or 4pm BST (London Time)
This free webinar is being put on by the Loyalty Fraud Prevention Association in cooperation with Sift Science.
When does it make sense to hire a team and build an in-house risk engine, or can external vendors, tools, operations and software prove just as tailored and intuitive? How does a layered fraud approach function seamlessly and effectively? Learn from experts in fraud that helped build abuse teams at Square, Google and Facebook.
This webinar will take place at 11am EDT (New York Time) or 4pm BST (London Time) on September 27th. Registration is free-of-charge, but registrations are limited in order to allow for Q&A. Register now to secure you place!
Kevin Lee, Trust & Safety Architect at Sift Science, is a seasoned fraud prevention expert. Prior to Sift Science where he spearheads Sift Science's machine learning fraud prevention architecture, his experience includes senior roles relate to risk and fraud at Facebook, Square and Google. Kevin has provided the Loyalty Fraud Prevention Association's with its informal motto: "Trust is earned in drops, but lost in buckets."
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.
Christopher Staab is Co-Founder of the Loyalty Fraud Prevetnion Association. Formerly at the International Air Transport Association (IATA), Chris has subsequently worked with numerous stakeholders across the globe in the loyalty industry to understand their issues and concerns relating to loyalty fraud.
Sift Science is a hyper-growth Series D company that facilitates the largest trust network of online businesses and consumers on the internet.
The Sift Science Trust PlatformTM uses real-time machine learning to accurately predict which users businesses can trust, and which ones they can’t. The upshot? Consumers know which companies they can entrust with their personal and financial information. And businesses can customize each user's experience based on their trust score – which leads to more revenue, higher conversion rates, and less fraud and abuse. Try it for free or learn more at: https://siftscience.com