RainbowRainbowApproach to Fraud Management Powers Seamless Shopping Experience for Customers

Key Results
100%
Automated Decisions
99.3%
Approval Rate
56%
YoY Chargeback Rate Reduction
Forter’s solution means less time worrying about fraud and more time focusing on how to add value to our business.
Michael Hoffman
Director of E-Commerce and Customer Service

The Problem

Rainbow is an international fashion retailer with more than 1,000 locations across the United States, including Puerto Rico, and the US Virgin Islands. The brand found success with a wide array of affordable fashion, and felt that their rules-based fraud prevention system held them back from efficiently dealing with fraud.

“A legacy or rules-based approach is simply not nuanced enough to ensure accurate and efficient fraud prevention.”

– Michael Hoffman

The Strategy

Rainbow sought a partner to take over the burden of fraud prevention by providing 100% full automation of the process. Rainbow’s goal was to ensure their new fraud prevention partner would maintain their high approval rate without jeopardizing the accuracy of the decisioning – a problem Forter was more than equipped to solve.

By leveraging AI and machine learning, Forter provides us an expert eye for the details of many forms of fraud, with full automation and higher accuracy.
Michael Hoffman

The Solution

Forter’s Payment Fraud Protection Solution has provided Rainbow with excellent results. Forter has elevated Rainbow’s approval rate to 99.3% and has completely automated approve/decline decisioning at the point of transaction.

Forter’s sophisticated machine learning model and proprietary soft linking technology - enabling the solution to connect personas, even when no two data points are in common - has also made it possible for Rainbow to better understand their ecosystem and address unique fraud pain points.

Fraud Attack Index
Forter’s Fraud Attack Index Available Now
Stay one step ahead of sophisticated fraudsters and learn all of the insights merchants need to better combat criminals in the year to come.