Featurespace claims to have implemented a breakthrough on its ARIC Risk Hub platform. By automating the identification of feature selection the platform can monitor and learn fraud patterns faster and apply the solution to a broader set of fraud related problems:
“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.”
Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group
