Why you should care
Banks are using AI to offer personalized services to customers.
Banks are exploiting artificial intelligence to target individuals with products and third-party rewards based on their specific needs and tastes, as part of a broader effort to strengthen customer relationships by homing in on the “segment of one.”
Analyzing customer data to set basic loan sizes and terms has become standard practice at most retail banks: They can dissect a user’s profile and recurring transactions to predict when they might want to take out an overdraft or insurance policy, for example. But some banks are starting to analyze customers’ ad hoc spending to offer discounts with retailers, restaurants and other partners that are tailored specifically to them. As well as the cross-selling opportunities that analyzing user data presents, banks also hope that personalized rewards via external partners will appeal to existing customers and help attract new ones.
It will be able to make projections on what you’ll be able to spend and save.
Chad West, head of marketing, Revolut
In July, Germany’s N26, a digital bank, relaunched its premium account to focus more on personalization, and now includes discounts with third parties such as flexible workspace provider WeWork and online travel booking site GetYourGuide. These are being offered indiscriminately at the moment, but the aim is to tailor services to each individual once the bank has gleaned a clearer picture of takeup. Meanwhile, rival Revolut is in the early stages of developing a machine learning product to help customers budget. At present, users must set their own monthly spending limit on individual accounts within the app.
“It’s going to look at [your] spending over the last three to six months,” says Chad West, Revolut’s head of marketing. “And it will be able to make projections on what you’ll be able to spend and save.”
That is something Spanish lender BBVA already offers customers via its Bconomy app, which incorporates the 50:20:30 budgeting rule. The concept is to spend 50 percent of income on basic needs, 20 percent on savings and use the remaining 30 percent as disposable income. By analyzing historic transactions, the app can alert customers if they are likely to run short on funds that month and then suggest moving money from a savings account, take out an overdraft or some other type of credit facility.
The bank is trying to go further still by developing a tool that will present each user with a personalized version of the app, which would give prominence to the functions or insights likely to be most useful to that individual on any given day. “Identifying these needs requires analyzing the user’s context — browsing history, transactionality, behaviors, etc. — through algorithms, similar to how Netflix and Spotify recommend content to their users,” says Ricardo Martín Manjón, BBVA’s head of data.
It is a trend set to play out across large swaths of the banking sector, according to a 2019 survey of nearly 800 banking executives by consulting firm Accenture. “Digital demographics [i.e., segmenting users’ online behavior] plus new flexible product configuration capabilities are getting banks much closer to the fabled ‘segment of one,’ where products and services are tailored to the individual in real-time,” the report said.
Banks have always used predictive modeling in consumer strategies, says Craig Macdonald, Accenture’s data monetization lead. But AI allows banks to “auto-learn from clients on how they react to different types of service and develop more advanced testing techniques,” he explains.
The high value of such intricate customer data has not been overlooked by Revolut. When it rolls out its Perks cash-back deals — which include discounts on coffee, restaurants and travel — to its 7 million customers, the company will initially fund the offers itself. But it hopes the rewards package will become popular enough that third parties will foot the cost of partnerships in the future. “We would hope to take our data to those companies and gain user discounts,” says West.
The widespread adoption of AI in this context, however, will rely on customer trust that personal data will be handled responsibly and ethically.
In July, the U.K. government’s Centre for Data Ethics and Innovation highlighted the potential of harmful “societal biases embedded in targeting criteria, algorithms and the data sets that underpin them.” In other words, there is a concern that algorithms reflect the unconscious bias of the humans that build them.
For financial services companies, that could include setting credit limits based on a customer’s race or gender, for example. One potential solution, the report suggests, is to introduce a layer of auditing to evaluate how companies are using AI and handling data.
In the case of banks working with external partners, Accenture’s Macdonald says the company is not aware of any instances where customer data has been exposed. “The major publicly disclosed data breaches in the past few years have been from hacking activity, not from data release from the marketing or advertising data transfer process,” he says.
However, in the U.S., greater automatic processing of personal information has sparked a rise in the number of class-action suits concerning the misuse of data, says Kate Scott, partner at law firm Clifford Chance’s financial services practice, adding that the U.K. may be “on the cusp” of a similar trend.
Companies using detailed transaction information to feed algorithms could run into trouble, she warns, either through misuse of personal data regulations, or the “creative” breaching of rules around treating customers fairly when promoting products and services.
“Those claims are clearly possible and I think as there is a growing use of those technologies, we will see more of those types of claims,” says Scott.
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