As published on PYMNTS May 20, 2020
False positives are the bane of fraud fighters everywhere, as they penalize legitimate customers due to the behavior of bad actors illicitly armed with credentials, card numbers and accounts.
“Turning away a good order is so damaging,” Rich Stuppy, chief customer experience officer at Kount, told PYMNTS. “Not only does your business lose the revenue, but you can drive a frustrated customer to your competitor — even worse, the customer might share [his or her] negative experience with others, damaging brand reputation.”
After the three months that restaurants have had, that’s simply unacceptable.
Saying that “… rules-based protection isn’t enough,” Stuppy added, “A blanket decision to turn away transactions based on one or two factors is a recipe for false positives, and one that can be avoided with advanced artificial intelligence. Next-generation AI combines both supervised and unsupervised machine learning to analyze billions of fraud and trust-related signals and to deliver accurate, automated decisions in milliseconds.”
This is the focus of the May Mobile Order-Ahead Tracker® done in collaboration with Kount, providing an immersion in MOA trends and technologies — in this case centering on using artificial intelligence (AI) and machine learning (ML) to streamline fraud prevention.
Data Theft Ruining Appetites
Quick-service restaurants (QSRs) are doing heavy mobile order-ahead (MOA) business right now — in other words, all QSRs are dealing with order volumes and logistical challenges never imagined even six months ago. Fortunately, the MOA network of restaurants, aggregators and drivers, was in place and working well when the entire country began living on deliveries.
Guess who else was in place and working? Yup. Crooks.
“Sixty-two percent of QSR customers are concerned about fraud when interacting with QSR apps, according to a recent study. This is a well-founded fear, given that 64 percent of American adults have been victimized by data theft,” the Tracker states. “Passwords or quick response (QR) codes may seem like solutions, but 40 percent of customers report being frustrated by these security measures because using them while ordering requires too many steps. Finding a balance between security and speedy transactions is thus a constant challenge.”
That’s the point at which AI and machine learning earn their spurs.
“AI-driven fraud detection systems can holistically analyze each transaction and compare included data points to every other data point in seconds,” the report states.
“These systems can also compare orders against every other transaction the QSR has processed and consider variables a human analyst might never notice to determine their likelihood of being fraudulent. An AI-based system might recognize a credit card being used in another’s account, for example, or that the same account has been entering different usernames and passwords over the course of several months. ML-enhanced systems bring new advantages … as they can learn from past transactions and automatically apply these rules to detect fraud.”
Static Rules, Say Hi To AI
With the food delivery market poised to generate $365 billion by 2035, that volume equates to millions of openings for hackers to exploit. Stopping crooks is now a critical function as the thin-margin restaurant sector watches every penny and attempts to reinvent a livelihood.
“Fraudsters can change tactics quickly, so any tool used to fight them needs to be just as fast. AI- and ML-based options could be the answer to reducing static rules’ frictions and subsequent manual reviews,” according to the May 2020 Mobile Order-Ahead Tracker®.