Case Study: Global Payments and Fintech Company achieves 92% reduction in false positives and 30% cut in manual review time with Fraud.net

Rules as a Service Slashes False-Positive Rates for Bank and Fintech Clients by 92%

Global Payments and Fintech Company, a cross-border payments processor with in-house fraud teams, was struggling with high false-positive rates and excessive manual review time. They engaged Fraud.net and adopted Fraud.net’s Rules as a Service/AI-powered rules to improve rule efficacy, reduce manual investigations, and streamline fraud identification across multiple countries and distinct user activity patterns.

Fraud.net reviewed and optimized the customer’s rules and tailored its machine-learning engine to automate screening of millions of transactions, rapidly flagging likely fraud with greater accuracy. The Fraud.net implementation cut false positives by up to 92%, reduced time spent on manual reviews and investigations by 30%, and greatly decreased the need to review flagged items individually.