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When Capital One rolled out its automated fraud-check texting system a few years ago, the bank was surprised to find how many possible answers its customers could find to a simple yes-or-no question.
While around 85 percent of people responded to the alerts about potentially suspicious credit card activity with a “confirm” or “deny” as directed, the rest of the replies ranged from fat-fingered typos to a “yep, that was me” or even intimate bits of travelogue—“yes, that’s the purse I bought in Philadelphia last weekend while visiting my sister”—says Ken Dodelin, the bank’s vice president of conversational artificial intelligence product development.
“I like to say that we launched the chatbot a long time ago—just, nobody ever told us,” he says. “Because people were chatting away with us, and we were on the other side not really prepared.”
Capital One wasn’t the only one feeling pressure to improve its technology. The retail banking industry has been increasingly embracing digital automation to keep its lead against financial tech upstarts like digital lenders SoFi and GreenSky—companies that understand how machine learning and personalization can improve customer experience.
Significant investment and institutional overhaul have been powering this move toward automation: Banks have been spending heavily on AI talent, operational upgrades and strategic tech partnerships. One recent report from the International Data Corp. found that the banking industry is the second biggest investor in AI technology worldwide behind retail. It’s expected to sink a total of $5.6 billion into applications like automated customer service agents and fraud prevention in the coming year.
As breakthroughs in AI research make software that can interpret varied voice and text commands, predict user actions and personalize messaging at scale, the industry is latching onto the opportunity to simultaneously cut costs and expand the functionality of one of its biggest expenses: customer service.
“The financial industry is uniquely ready to make a move in the space and bring that computational experience,” says Jason Mars, CEO of Clinc, a startup that makes chatbots for financial institutions like Barclays and USAA. “They’re trying to bring computational AI into the call center to solve some of their biggest pain points—the pain points that have to do with providing customer care and staying relevant in an industry where folks are now moving to this new user interface.”
Banks take the lead
The banking industry is by no means alone in chasing the cost-cutting dreams that the current boom in analytic-AI technology promises. From hyper-personalized marketing messages to leaner supply chains, companies of all stripes are scrambling to scale their data and analytics tools across all divisions in the face of Silicon Valley competition.
But what is different from categories like retail and media is that the incumbent banking industry actually seems to be winning. Rather than bowing to fin-tech startups, big financial institutions have used a combination of savvy collaboration, acquisitions and a willingness to shell out for high-demand talent to stay on top or partner with newer rivals. Some reports also credit the banking industry’s mastery of regulatory compliance issues, which are more restrictive than those of other industries and therefore make banks harder to disrupt.
In Capital One’s case, it took the bank some time to figure out how to respond to those unexpected communications from its customers. The company spent the ensuing months becoming more like a tech company—marshaling engineering talent, upgrading back-end infrastructure and building out AI capabilities like machine learning.
One of the most prominent results of that process is Eno (one spelled backward), Capital One’s genderless text-based assistant, which uses a form of AI called natural language processing to capably understand a range of normal human communication patterns.