The window financial institutions (FIs) have to determine "good" customers from "bad" lasts milliseconds. As fraudsters steal their unwitting victims' online identities, intercept SMS messages, mask device locations to commit payments fraud, banks and other firms need to be able to spot "signs" hidden in the eCommerce deluge that can separate genuine transactions from fraudulent ones. It's a $40 billion problem, that, as Dave Excell, founder of Featurespace, told Karen Webster, needs deep learning networks and a range of automated advanced technologies and models to construct the best lines of defense against the fraudsters. Two new patents, leveraging those advanced technologies, can help FIs pinpoint behavioral changes and identify high-risk behavior -- stopping fraud and financial crime before it happens. Featurespace said Monday (July 12) it had filed those two global patents, aimed at transforming network architecture and risk scoring to protect customers and accounts.

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. "The significance of this development goes beyond the scope of addressing enterprise financial crime. "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. 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. 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 optimized 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 behavior. 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 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 company's Adaptive Behavioral Analytics. Detecting fraud before the victim's money leaves the account is the best line of defense against scams, account takeover, card and payment fraud attacks. 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.