Featurespace Introduces Adaptive Behavioral Biometrics to Solve Complex Fraud Attacks in Digital Channels
AMSTERDAM–(BUSINESS WIRE)–From Money
20/20 Europe, Featurespace introduced Adaptive Behavioral
Biometrics, which uses in-session behavioral data collected from digital
channels in real time to detect and prevent fraud during customer
onboarding and digital sessions.
Digital customer onboarding presents a significant challenge for banks,
insurers, and other financial institutions because there is no
historical information to accurately determine if an applicant is
genuine or a criminal. These organizations are also bombarded with new
threats to existing customers, such as malware, as well as account
takeover and man-in-the-middle and phishing attacks.
Available through the ARIC Fraud Hub, Adaptive Behavioral Biometrics
models track user-specific features collected ahead of a transaction.
The models consistently self-learn from each interaction to produce a
unique session fingerprint that indicates an individual user’s usual or
unusual behavior and provides fraud analysts with an easy-to-understand
visual profile that even detects phishing and malware activity generated
by genuine customers.
“We’re attuned to the evolution of fraud and leverage our market-leading
models and technology to continue to deliver the latest and most
advanced fraud prevention and detection tools,” said Martina King, CEO
at Featurespace. “There has never been a more important time to support
our customers in their drive to prevent fraud loss and reduce customer
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 risk
prevention and creator of the ARIC™ platform, a real-time AI machine
learning software that risk scores events in more than 180 countries to
prevent fraud and financial crime.
The ARIC platform combines unique adaptive behavioral analytics and
anomaly detection to automatically identify risk and catch new fraud
attacks and suspicious activity in real-time. The increased accuracy of
understanding ‘good’ behavior strikes the balance between improving the
detection of suspicious activity, while also reducing the number of
false alerts, to improve operational efficiencies.
Michael Touchton, Featurespace
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