A constant drumbeat in banking is that businesses require ever more data analytics to remain informed and competitive. Yet Envestnet|Yodlee acknowledged its data offerings were out of tune with customer expectations.
Its Envestnet Envision IQ platform, launched in May, offered up a variety of information concerning a bank’s customers and products, but it had no focus, according to a top executive at the data aggregator.
“Unfortunately, sometimes in data analytics we don’t know any questions, but we give you all the answers and we make you figure out what your answer is,” said Frank Coates, executive managing director of Envestnet. “The easiest way to answer a question is, let them ask the question and give them the right answer.”
With that feedback in mind, the firm recently launched a business intelligence platform, Envestnet Intelligence for Financial Institutions. The goal, Coates said, is to make accessing data as simple as selecting a song to play, while bringing institutional analytics power down to the branch level.
Branch managers will be able to create their own “playlist” of questions to be answered daily, he said, and the system will record and categorize questions asked into favorited questions and most-asked questions.
The ability to specify branch and even local customer activity removes the step of relying on home-office bankers to analyze data, Coates noted. He said he hopes bankers will use the tool to explore what kinds of questions they should be asking by looking at related questions.
“We hope it also spurs people to say stuff like, ‘I hadn’t really thought about net new customer. That’s a question other branch managers are asking at my company — why?’ ”
Executives can also ask why their branch managers aren’t looking for certain statistics and can program the platform to put questions on each manager’s playlist for them.
“It creates alignment across the organization,” said Dave Lieberman, vice president and senior solution consultant at Envestnet.
Powering the tool is artificial intelligence, which will be plugged into all of a firm’s channels. As a result, a banker will be able to search a platform that’s connected to desktops, mobile devices and Amazon’s Alexa voice assistant. When queried, the tool produces narrative-like written responses as well as PDF reports.
If bank employees are asking questions that a voice assistant doesn’t have the ability to answer, those questions can then be added to the platform’s library by Envestnet employees training the bot.
Such tools could make the price of data aggregation more attractive to banks, said Mark Schwanhausser, director of digital banking at Javelin Strategy & Research.
“Traditionally, there has been the promise of a stickier relationship that the consumer will say, ‘You provide me a financial hub, so I’m going to keep coming back to you and make you my primary. If I’m thinking about buying another product I’ll give you the first shot at it,' ” Schwanhausser said. “But it’s a soft return on investment and it’s one that many bankers are dissatisfied with.”
Some industry observers warned the approach had to first prove its worth in practical terms.
AI that’s effective in financial services is always one step ahead of the banker — sending tearsheets of information about companies in advance of visits, without having been asked, said Joe Salesky, CEO of CRMNext, a customer relationship management software provider.
Bankers don’t just want to be informed of their branch’s metrics every morning, he said; they need to be alerted when their branch numbers fall behind a certain threshold, much like how AI is helping alert banks of suspicious activity on client accounts.
“They want an intraday message letting them know they are below a trend to get where they need to,” Salesky said. “Then they want to send a message to their team that we need to catch up.”
Whatever the required refinements, layering data analytics with AI adds value for aggregators, consumers and banks, said Brandon Dewitt, chief technology officer at MX, an Envestnet competitor in account aggregation and data analytics.
An analogy to its development, Dewitt said, is how the directory framework of searching was once the leading concept in the search engine business.
“You pick a category and then the graph of potential subcategories is smaller and thus a more focused ‘ad’ can be displayed,” Dewitt said. “But that went away largely with page-rank where organizations use metadata and previous user search statistics to find the most likely result based on all current knowledge of the search and person.
“We are really focused on the ‘page-rank’ generation of ‘needle in the haystack’ problems instead of building a better dictionary.”
