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:
“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."
About Featurespace – www.featurespace.com
Headquartered in the U.S. and U.K. and with offices in Atlanta, Cambridge and London, Featurespace™ is the world-leader in fraud prevention and creator of the ARIC™ platform, a real-time AI machine learning software that risk scores events in more than 180 countries.
The ARIC platform combines adaptive behavioral analytics and anomaly detection to automatically identify risk and catch new attacks as they happen. The increased accuracy of understanding behavior strikes the balance between improving fraud and risk detection and operational efficiencies, while also reducing the number of genuine transactions that would be incorrectly declined by as much as 70 percent.