Machine learning may find fraud victims before the scammers do

LAS VEGAS--It's become a common analogy for the use of predictive analysis in business technology: Wayne Gretzky became the best hockey player of his generation not because he skated to where the puck was, but because he skated to where the puck was going. Similarly, financial institutions are hoping to get ahead of the growing and seemingly insurmountable problem of payment card fraud not just by looking at who cyber-attackers are going after currently but who they are likely to defraud in the near future. At the Black Hat USA conference here last week, a pair of researchers -- one from Royal Bank of Canada and the other from a service provider that focuses on dark web intelligence -- presented on their joint effort to use machine learning, predictive analytics and transactional data together to get a handle on which cardholders might be the next victims of cyber-crime. With the vast stores of payment card, transactional, personal, demographic and historical fraud data to work from, it would seem that card-issuing banks already have a lot of information with which to work to help them determine the direction of fraudulent activity. The problem with having so much data is it is hard to find the right information at the right time.