Link Money Spearheads Secure Open Banking and Better User Experiences in the U.S. with Sift

Open banking is poised to remove the complexity of the payments value chain, making online payments safer, faster, and cheaper. Link Money is an early adopter of this trend by revolutionizing payments in the United States with the first Pay by Bank solution focused exclusively on the U.S. market. Their solution harnesses open banking to offer merchants an alternative payment option that is both low-cost and convenient for customers who want to pay using their bank accounts. Link Money makes Pay by Bank as easy as using a credit or debit card while saving merchants up to 70% in payment processing fees. 

Link Money opted for Sift due to the power of Sift’s Global Data Network, industry-specific behavior models, and 16k risk signals in order to expand into new industries with a leg up. Leveraging the Sift Score API, Link Money can seamlessly combine consortium data from the Sift Global Data Network with their own data sources, including banking data, and information from merchants, allowing for precise fraud risk assessments. 

By integrating Sift’s signals into their data science framework, Link Money can conduct thorough analyses that allow them to protect against various types of fraud and accept more high-value transactions. The team also uses the Sift Platform to facilitate post-transaction monitoring, covering key aspects such as account signals, behavior analysis, purchase velocity, interaction speed, and connected users.

By leveraging Sift, Link Money accelerated their industry expansion. The signals and data Sift provides has not only saved the Link Money team time, but also allowed them to protect their brand and establish trust in open banking.

Within a year, they saved $3 million for one of their ticketing merchants by effectively stopping sophisticated fraud rings and continuously adapting their responses. By implementing Sift, the team has been able to identify and prevent fraud rings involving instances of single bank accounts rotating among several users, multiple devices, or IP addresses. Sift also prevented scenarios of individual users with multiple bank accounts attempting to recycle accounts to evade detection.

Sift also helped Link Money quickly learn and adapt to new customer bases. When onboarding a new merchant earlier in the year, they initially experienced a wave of fraud. But within six weeks, fraud volume dropped by 90%. The speed at which Sift’s models learned the new merchant’s patterns proved to be a major value-add, giving the team confidence in scaling without sacrificing security.