Featurespace Launches Automated Deep Behavioural Networks

Featurespace introduces Automated Deep Behavioral Networks for the card and payments industry, providing a deeper layer of defence to protect consumers from scams, account takeover, card and payments fraud, which cost an estimated $42bn in 2020.

“The significance of this development goes beyond the scope of addressing enterprise financial crime. It’s truly the next generation of machine learning,” said Dave Excell, founder of Featurespace.

The Invention

A breakthrough in deep learning technology, this invention required an entirely new way to architect and engineer machine learning platforms. Automated Deep Behavioral Networks is a new architecture based on Recurrent Neural Networks that is only available through the latest version of the ARIC™ Risk Hub.

The Challenge and the Discovery

Deep learning technology has various applications, such as in natural language processing for the prediction of the next word in a sentence, however its use in preventing fraud in card and payments fraud detection has not been optimised to protect companies and consumers from card and payments fraud. With this invention, that challenge is solved.

Transactions are intermittent, making contextual understanding of time critical to predicting behaviour. Previously, building effective machine-learning models for fraud prevention required data scientists to have deep domain expertise to identify and select appropriate data features – a laborious, yet vital step.

Featurespace research developed Automated Deep Behavioural 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 company’s Adaptive Behavioural Analytics. Detecting fraud before the victim’s money leaves the account is the best line of defence against scams, account takeover, card and payment fraud attacks. For the following groups, the benefits of Automatic Deep Behavioural Networks include:

Consumers:

• Enabling genuine transactions with reduced verification; and
• Automatically identifying scams, account takeover, card and payment fraud attacks before the victim’s money leaves the account.

Data scientists:

• Automatically discovering features in transaction events;
• Pushing machine learning logic through the entire modelling stack;
• Leveraging the irregularity of human actions to identify anomalistic behaviour; and
• Retaining all of the discoveries of Featurespace’s Adaptive Behavioural Analytics.

Card and Payments Industry:

• Improving risk score certainty across all transactions (fraud detection during the transaction is increased and genuine behaviour 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);
• Reducing step-up authentication;
• 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.

Excell continued: “As real-time payments, digital transformation and consumer demand require the instantaneous movement of money, our role is to ensure the industry has the best tools for protecting their organizations and consumers from financial crime. I am immensely proud of our research team and their dedication to machine learning innovation on behalf of our customers.”