Featurespace reveals behavioural networks for account takeover and payments fraud protection

Via this initiative, the aim is to provide a layer of defence to protect consumers from scams, account takeover (ATO), card and payments fraud, which cost an estimated USD 42 billion in 2020. This invention is a new architecture based on Recurrent Neural Networks that is only available through the latest version of the ARIC Risk Hub.

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. 

The benefits for consumers include:

• Enabling genuine transactions with reduced verification; 

• Automatically identifying scams, account takeover, card and payment fraud attacks before the victim's money leaves the account.

The benefits for data scientists are:

• Automatically discovering features in transaction events;

• Pushing machine learning logic through the entire modelling stack;

• Leveraging the irregularity of human actions to identify anomalistic behavior; 

• Retaining all of the discoveries of Featurespace's Adaptive Behavioural Analytics.

The card and payments industry benefit from:

• Improving risk score certainty across all 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; 

• Delivering real-time scoring with low latency response times for business-critical enterprises, even under surge conditions.