Fraud Prevention Tools: Building vs. Buying - Fraud Systems Webinar Recording

Date: 27 Sep 2018    Location: Webinar    Delegates:



Webinar: held Thursday, September 27th at 11am Eastern (New York Time)
or 4pm BST (London Time)


This free webinar was 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? Request the recording of this session to learn from experts in fraud that helped build abuse teams at Square, Google and Facebook.

Request the Webinar Recording here.




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."




Christopher Staab is Co-Founder of the Loyalty Fraud Prevention 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