How AI Is Shaking Up Banking and Wall Street
Meet Your New Robot Mortgage Lender
ONE THEORY HAS ARISEN in the decade since the subprime mortgage crisis: Machines may be better than humans at giving out home loans. A new Fannie Mae survey of mortgage lenders found that 40% of mortgage banks have deployed A.I.—using it to automate the document-heavy application process, detect fraud, and predict a borrower’s likelihood of default. San Francisco–based Blend, for one, provides its online mortgage-application software to 114 lenders, including lending giant Wells Fargo, shaving at least a week off the approval process. Could it have prevented the mortgage meltdown? Maybe not entirely, but it might have lessened the severity as machines flagged warning signs sooner. “Bad decisions around data can be found instantaneously and can be fixed,” says Blend CEO and cofounder Nima Ghamsari. While banks are not yet relying on A.I. for approval decisions, lending executives are already observing a secondary benefit of the robotic process: making home loans accessible to a broader swath of America. Consumers in what Blend defines as its lowest income bracket—a demographic that historically has shied away from applying in person—are three times as likely as other groups to fill out the company’s mobile application. Says Mary Mack, Wells Fargo’s consumer banking head: “It takes the fear out.” —Jen Wieczner
A New Edge For Pro Investors…
IN THE WORLD OF FINANCE, there’s been such an explosion of data collected over the past decade that even those twenty-something analysts working around the clock don’t stand a chance of being able to process it all. But machines might. Bloomberg, FactSet Research Systems, and Thomson Reuters have all developed an array of data science tools and techniques—including machine learning, deep learning, and natural language processing (NLP)—to quickly unearth valuable insights for thousands of financial professionals.
Bloomberg was a pioneer of sentiment analysis (an example of NLP), which it began developing around a decade ago, in which machine-learning techniques are used to flag a news story or tweet as being relevant to a stock and assign a sentiment score. A.I. is also spreading to wealth management. Investment groups have more than quadrupled their number of “alternative data” analysts over the past five years, as asset managers scramble to unlock the potential of trading signals contained in website scrapes, language analysis, credit card purchases, and satellite data. Firms reported to be using A.I. for investment research include BlackRock, Fidelity, Invesco, Schroders, and T. Rowe Price. BlackRock, the world’s largest asset manager, has been a forerunner in adopting A.I. and is setting up a BlackRock Lab for Artificial Intelligence. —Scott DeCarlo
Percentage of people who are afraid of robots taking over their tasks, according to Pew Research.
…And For The Amateurs Too
“ROBO-ADVISER” SERVICES, offered by startups like Betterment and traditional discount brokerages like Charles Schwab, are already using A.I. to serve the investing masses. Their low-fee investment tools rely on algorithms to determine how your assets should be split among stocks, bonds, and other assets, based on your needs and your stomach for risk. Their A.I. can automatically rebalance your portfolio; it can also nudge a (nonrobotic) adviser to call you when the algorithms predict you need help with tax strategy or estate planning.
The next frontier: A.I. smart enough to help savers make good decisions about long-term, buy-and-hold investments. Bank of America Merrill Lynch and Morgan Stanley are among the bigger players in an emerging discipline known (awkwardly) as quantamental analysis. They aim to combine the quantitative processing for which basic A.I. is best suited (basically, the capacity to spot patterns in gargantuan loads of data) with additional algorithms trained in the sophisticated analysis associated with super smart humans—assessing, say, the growth potential of an industry or the strategic acumen of a company’s management. Machine learning could eventually enable a quantamental system to learn from its mistakes. The ultimate goal: Warren Buffett–like stock-picking wisdom at low prices—and perhaps a name catchier than “quantamental.” —Matt Heimer
CORRECTION: An earlier version of this article incorrectly stated that Robinhood offered robo-adviser services.
A version of this article appears in the November 1, 2018 issue of Fortune as part of the article, ’25 Ways A.I. Is Changing Business.’