Navigating the Payments and Fraud Landscape: Global Data, Local Insights - Europe

Friendly Fraud
First-Party Misuse
Cybersource; Merchant Risk Council; Verifi
Jun 16, 2022

How well does your fraud performance stack up against the latest global performance metrics? And what new fraud trends and tactics should be on your priority list? 

In this webinar, Merchant Risk Council’s VP of Programs and Technology Tracy Kobeda Brown, Cybersource Managed Risk EMEA Regional Lead Mark Strachan, and Verifi Head of International Merchant Sales and Business Development Gabe McGloin will explore the answers to these important questions, by diving into key findings and results from the recently launched 2022 Payments and Fraud Report.

Join us to hear these three fraud experts:

  • Discuss what the report’s latest fraud and payment metrics and benchmarks mean—and how you can use them to evaluate and improve your own fraud programs and strategies.
  • Provide new insights into the full business impact of fraud and how you can better protect your organization.
  • Share the latest on which new payment methods are gaining the most traction in different parts of the world—and what social and economic factors are driving their adoption.

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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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