AI could help prevent awkward credit card moments
IT’S an experience many shoppers have had: You’re in line at the shop prepared to make a purchase, only to have your credit card declined for no apparent reason. A perfectly legitimate charge has been flagged as fraudulent, and the result is an agitated customer and a retailer with unsold merchandise.
MasterCard has now turned to artificial intelligence to better differentiate between real and mistaken fraud, hoping to tamp down on the former while allowing the latter to go through. It’s the latest financial services company to see the potential for the burgeoning field of machine learning to improve security on its network and enhance the customer experience.
Financial institutions have for years collected data on cus- tomers’ habits and routines, and used the information to pinpoint cards that may have been compromised. That’s the reason your card may be declined if you make an unusually large purchase, shop at a store for the first time, or buy gas in a place far from home. The system is deciding whether that behaviour seems normal according to predetermined questions, then accepting or declining the purchase based on its decision.
Software to combat fraud
“The old method was using tests and thresholds and other sorts of rules. With a rules-based approach, you get a tremendous amount of false positives,” said Todd Marlin, a principal in Ernst & Young’s forensic technology and discovery services practice.
Machine learning can help fraud detection systems be- come smarter about what fraud actually looks like, both across the network and on an individual level. For example, the system might detect that you haven’t shopped at a particular merchant in the past, but still accept the purchase because customers with a similar spending history shop there often. Or perhaps you travel to a certain state or country often enough that the system learns purchases there are likely to be legitimate.
Citing a survey from Javelin Advisory Services, MasterCard estimates that $118 billion in sales were declined due to falsely identified fraud in the United States in 2014 – well more than the $9 billion lost to actual instances of fraud. That’s a large sum of money that retailers – and credit card purveyors like MasterCard – could be pocketing.
Mastercard’s new Decision Intelligence software pulls in data, sometimes hundreds of pieces of data, about a specific transaction at the moment a customer swipes his or her credit card. The system then combines all of that information to yield a score indicating how likely the transaction is to be fraudulent. Each score builds on the one before it and informs the one after it such that the computer’s algorithm gets better at detecting fraud without a programmer having to engineer every change. The company called it “the first use of AI being implemented on a global scale directly on the MasterCard network”.
For the last decade, Visa has deployed its Visa Advanced Authorization system to detect fraud. The volume of data and the speed at which it’s processed has increased considerably over that time, said Mark Nelson, the company’s senior vice president of risk products and business intelligence. Visa parses through those large sets of data to discern what characteristics distinguish legitimate and fraudulent spending, and then uses those characteristics to assess the veracity of future purchases.
Digital payment platforms are also embracing machine learning, though fraud remains more difficult to detect in online or mobile commerce. PayPal has developed its own artificial intelligence software to combat fraud, allowing the company to move from a system that can analyse tens of data points to one that can analyse thousands.