Friendly Fraud 2.0: Mitigating Against Returns Fraud, Liar Buyer & Promo Abuse

Attack Types
Friendly Fraud
First-Party Misuse
Uri Arad -- Identiq and Karisse Hendrick -- Chargelytics Consulting
Jun 15, 2022

In this fireside chat style webinar, join Karisse Hendrick, host of the popular Fraudology podcast, as she draws on the friendly fraud lessons she learned from the 2008-9 financial crisis, and her wealth of experience since, to illuminate the challenges fraud fighters are facing today. She’ll be swapping tips with Uri Arad, Identiq’s VP Product, who cut his friendly fraud teeth at PayPal back in the day when customers started taking advantage of new payment methods to cheat their way out of payment.

The fight against unfriendly fraud is undoubtedly going to be with us for a long time to come. With this webinar, fraud fighters can keep up to date with the latest trends and tricks, receive reassurance that they’re certainly not alone, and get some pointers about what might help their company stay ahead of the first-party misuse problem.

Learning Objectives:

  • Understand more about the current state of first-party misuse, and what’s causing the increase

  • Put the current situation in the context of previous times first-party misuse has spiked, and learn lessons from those occasions
  • Get tips about what might be effective in combating this developing problem

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Related Resources

Mar 08, 2023
Customer Recognition System - A New Tool of Detecting Fraud

Today, link analytics has been widely used across a variety of applications and industries (e.g., telecommunications, social networking, healthcare, finance ) to identify or predict the association of different entities behind the scenes. Companies with multiple product offerings use this technology to learn from their customers’ data to provide better user experiences. 

At Intuit, our customer recognition system (named Core ID, internally)  is focused on finding out if one customer or one family/close cluster uses one or more entities to register many accounts for products, such as QuickBooks Payments and QuickBooks Payroll customers. 

Normally, if the customer is identified with one set of entities, we can use existing solutions for ID-mapping, which rely on “exact” matching among entities to create clusters and graphs. However, this absolute linkage will fail if the customer is associated with multiple entities or changes entities (device IDs, IP address, etc.). 

To solve this problem, we have devised a methodology for recognizing one customer, or one household, from different angles by applying several AI-driven technologies. 

Intuit’s customer recognition system reveals relationships among different entities, serving as a complement to existing linkage-based graph analytics to more quickly identify or predict the association between customer accounts. Understanding these underlying connections more quickly is one strategy for building long-lasting customer relationships.

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