U.K.-based Featurespace, which offers anti-fraud systems for financial institutions (FIs), on Thursday (Feb. 25) released its “Automated Deep Behavioral Networks” package. In a press release, the company said the new product, intended for the card and payments industry, provides a “deeper layer of defense to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42 billion in 2020.”
The new product is “truly the next generation of machine learning,” said Dave Excell, founder of Featurespace. The company said that it involves “a breakthrough in deep learning technology” that is capable of pinpointing potential fraud before the victim’s money is removed from their account. That serves as “the best line of defense against scams, account takeover, card and payment fraud attacks,” the release stated.
Featurespace said that its new system “offers multiple machine learning solutions for fraud and anti-money laundering (AML) analysts to spot suspicious activity and prioritize alerts.” The company said the system has the ability to recognize legitimate customers without impeding their activity or adding payments friction for the consumer. The product automatically identifies scams, account takeovers, and card and payment fraud attacks, increasing payment security. This also benefits the card and payments industry, according to the release, by “improving the detection of high-value, low-volume fraud (and also detection of low-value, high-volume fraud), and improving risk score certainty across all transactions (and) fraud detection (while) delivering stable, real-time scoring.”
In November, Featurespace teamed up with CSI to create products to fight money laundering. “As criminals relentlessly target financial systems, organizations require cutting-edge technology for fraud detection and risk management,” said Kurt Guenther, CSI’s group president of business solutions. “By partnering with Featurespace, we’re providing our customers with a powerful AML solution that leverages the latest in machine learning to fight illicit activity and ensure compliance.”
“The ability to understand genuine customer behavior allows banks and credit unions to more accurately detect anomalies and additional suspicious activity so that only the most worthwhile alerts are passed along for review, while also reducing false positives and bringing more financial crime to light,” said Excell at the time.