To identify potential cases of fraud, Wang’s team analyzes historical payment data to identify features that may indicate an attempted scam. Things like what type of device the requester is using, what country the request originates from and details from the user’s PayPal profile all can be correlated with fraud. The team uses this data to build machine learning algorithms that assess each transaction for potential signs of fraud. Over time, the algorithm learns and sharpens its predictions. Given the evolving nature of threats to PayPal users, this learning approach to predictive data analysis has always been the goal of Wang’s risk modeling team. But she said it’s only been possible to implement in the past five years or so. Before that time, the computing power simply wasn’t available to run complex algorithms on such large volumes of historical data…