Featurespace Launches Automated Deep Behavioral Networks
Summary
LONDON & ATLANTA & SINGAPORE--(BUSINESS WIRE)--Today, Featurespace introduces Automated Deep Behavioral Networks for the card and payments industry, providing a deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020. Transactions are intermittent, making contextual understanding of time critical to predicting behavior. Featurespace Research developed Automated Deep Behavioral Networks to automate feature discovery and introduce memory cells with native understanding of the significance of time in transaction flows, improving upon the market-leading performance of the companys Adaptive Behavioral Analytics. • Improving risk score certainty across all transactions (fraud detection during the transaction is increased and genuine behavior is more accurately identified to facilitate the acceptance of more transactions); • Providing performance uplift for all payment types, including card and ACH/BACS, wire, P2P and faster payments; • Improving the detection of high-value, low-volume fraud (and also detection of low-value, high-volume fraud); • Providing strict model governance documentation, with explainable logic, fair decision making and reason codes; and • Delivering stable, real-time scoring with high throughput and low latency response times for business-critical enterprises, even under surge conditions. I am immensely proud of our research team and their dedication to machine learning innovation on behalf of our customers.” Featurespace™ is the world leader in Enterprise Financial Crime prevention for fraud and Anti-Money Laundering.