Transforming Fraud Prevention with Trusted Identities

Identity Fraud
Feb 23, 2022

Identity underlies every transaction. With rising levels of fraud, false declines, and chargebacks, it's time to rethink the concept of "identity" within the context of digital commerce. Pipl will share why the traditional concept of identity is failing merchants, payment processors, and ecommerce customers alike—and how it should change. We'll introduce the new paradigm of trusted identity, describe what comprises trusted identities, and provide real-life examples of how trusted identities are already being used to prevent fraud, combat chargeback and friendly fraud, and extend trust into the ecommerce lifecycle.

Learning Objectives:

• Understand the importance of deep connections between identity data fragments
• Identify trust signals and be able to recognize a trusted identity
• See how the concept of trusted identity is being applied across manual review, chargebacks, friendly fraud, and third-party fraud

Some content is hidden, to be able to see it login here Login

Blue-tinted background of a man watching a webinar

Host a Webinar with the MRC

Help the MRC community stay current on relevant fraud, payments, and law enforcement topics.
Submit a Request

Publish Your Document with the MRC

Feature your case studies, surveys, and whitepapers in the MRC Resource Center.
Submit Your Document

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.

Cookies help us improve your website experience.
By using our website, you agree to our use of cookies.