Can AI pick a customer's top-of-wallet card?

Banks and payment companies are already comfortable using artificial intelligence to guard against fraud. The tech could soon be just as welcome in creating payment products and matching them to potential customers.

Emerging technology can better match consumer preferences to offers in a self-directed manner to personalize the perks that issuers can use to lure consumers.

“We’re just scratching the surface with machine learning for the purpose of acquisition,” said Nadine Murray, senior vice president of strategy for Even Financial, a search, comparison and recommendation engine for financial services.

Murray recently joined the New York-based Even, a fintech that works with partners such as Credit.com, The Penny Hoarder and Empower to optimize payment, card and other financial product recommendations from Prosper, Lending Club and Goldman Sachs’ Marcus. This is achieved via an API connection as a way to avoid brick and mortar bank branches and to encourage omnichannel use. Earlier in her career, Murray worked in digital marketing programs at JPMorgan Chase, Citigroup and American Express.

Her new role comes at a time when the amount of consumer financial and payments data is increasing, as is the data’s ability to quickly add contextual and relevant information for issuers that can better match recommendations with user preferences.

That can include suggestions for payment cards, marketing and other emerging payment and financial product verticals that can be delivered in real-time. The recommendations aren’t direct loyalty programs per se, but can be part of a recommendation that includes a number of factors that accompany a payment program, card or financial service.

“The use of data has always been important as a way [to manage] programs,” Murray said. “It’s the magnitude of data that’s available that’s enabling the learning.”

Artificial intelligence has become increasingly popular among financial institutions and payment companies over the past two years, given its ability to manage and analyze large amounts of data, a practice that improves over time as more data accumulates about specific uses, accounts and relationships with different companies.

AI has found a welcome use case in managing security risk. Citigroup, for example, has partnered with Feedzai to match new payments with past transaction records to quickly spot possible errors or fraud. AI is also being used to thwart digital attacks on payment systems.

Customer service is also being improved by using AI to power voice-assisted service and self-directed customer service, along with product recommendations and as a fuel to migrate customer acquisition to an experience that resembles Netflix, Amazon or Google more than a card issuer. Facebook, for example, has become a popular venue for bot-driven customer service.

Additionally, more creative uses for AI are emerging, such as voice controlled payments and in-car shopping based on a rider's travel route.

“If you think about natural language processing and connections with consumers in real-time and couple that with AI techniques such as machine learning, you can get to a point where you can take recommendations to consumers to another level,” said Tiffani Montez, a senior analyst at Aite Group.

As consumers answer questions about rewards or card preferences, an AI-driven analysis can take place in the background on how that consumer spends money to determine whether a miles-based perk, cash-back or other travel rewards would best fit that particular consumer, Montez said, adding this can make preapprovals potentially more effective because there is more analysis of backward looking payments along with current data.

“It’s early yet for that kind of use because most of the adoption of AI has been in two directions. Lots of people are using it to power chat bots or interactive assistants, while others are using it for AML and fraud,” Montez said.