Aite Group, a global research and advisory firm, recognized Featurespace™ as Best-in-Class in its 2019 report on fraud and AML machine learning platform vendors.

Download a complimentary copy of the report here.

The leading machine learning Adaptive Behavioral Analytics risk management company, Featurespace was recognized for its “best in class” product and  ranked top overall in client service for its ability to provide robust service, support and value to clients. Featurespace also received exceptional recognition from Aite and its clients for:

Model performance: “…head and shoulders above others tested… with a 63% reduction in false positives and a 177% increase in CNP fraud detection.”

Product performance: “Featurespace significantly outperformed four other leading machine learning platform vendors in a head-to-head value test…”

Leading expertise: “…since many fraud trends tend to hit Europe a few years before they come to the U.S., Featurespace was already solving problems that banks in the U.S. hadn’t seen yet.”

“Our report provides a holistic analysis of participating vendors and identifies market leaders in their respective sectors,” said Julie Conroy, research director for Aite’s Retail Banking practice. “Featurespace’s technology is truly next-generation in fraud detection and mitigation, as well as reducing false positives, and we are delighted to recognize them as Best-in-Class.”

“We’re very grateful to Julie and the Aite team for their comprehensive research and analysis of the market. To be recognized as Best-in-Class by such a brilliant group is truly an honor,” said Martina King, CEO of Featurespace. “We invented adaptive behavioral analytics and have been changing the game ever since. This recognition reflects our product excellence and reinforces our commitment to preventing fraud for our customers and the industry.”

The report was released the same month that Featurespace’s ARIC Fraud Hub was named the Anti-fraud or Security Solution of the Year by the FS Tech Awards for a second consecutive year.