You Can Have It All: Minimize Fraud, Boost Sales & Balance Risk

Data analysis
Jun 07, 2022

Many - if not most - enterprise retailers are overpaying for fraud solutions and chargebacks, as well as losing out on millions of dollars in sales because of suboptimal fraud strategies. Preventing fraudulent purchases from being approved is the most important goal of any merchant’s fraud strategy, but it also poses a dilemma when a good customer’s legitimate transaction is blocked. Eradicating false declines and getting as many good customers as possible through check-out is challenging, but can be optimized through productive, data-driven discussions with the payments supply chain to rectify any issues and, ultimately, supercharge approval rates.

In this presentation, payments experts will showcase the importance of benchmarks and data-science in helping merchants not only assess, but also scrutinize the performance of their fraud suites. It will also walk through what a data-driven supply-chain engagement strategy can do to ultimately boost digital sales.

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.