Defeat Synthetic Fraud with a Fully Integrated View of Identity

Attack Types
Identity Fraud
TransUnion, Neustar
Apr 01, 2022

Brands lose billions of dollars every year to account origination and account takeover fraud because they cannot reliably tell the difference between consumer and synthetic identities in remote interactions.

Fraudsters gravitate to synthetic identities because they can easily bypass fraud systems that rely on verifying the linkages between individual identifying attributes (e.g., name, address, phone, etc.) As a result, they can evade detection for long periods of time, building up credit and reputation before committing costly "bust-out" fraud. 

Defeating synthetic fraud requires a holistic and stable understanding of the strength, tenure, and frequency of all linkages between a consumer's identifying attributes.

This presentation highlights ways to do exactly that.

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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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