What does "machine learning" really mean? Step inside for a closer look.
In the 2015 financial crisis drama The Big Short, quirky Wall Street sage Michael Burry — played by Christian Bale — stares intently at a spreadsheet, crunching numbers in his head.
His ultimate realization? A big chunk of U.S. subprime home loans is at risk of defaulting. And spoiler alert here: Burry winds up cashing in — big time — by betting against the housing market and tossing more than $1 billion of his investors’ cash into credit default swaps before the American housing bubble burst in the late 2000s.
Fast forward a decade later, and it’s less likely a guy like Burry would be the one to notice a major financial trend or an under-the-radar investment opportunity.
Instead, that’s increasingly the job of artificial intelligence.
Experts say the entire banking and financial services industry — from trading to wealth investment to fraud prevention — will be radically transformed by machine-learning in the years ahead, be it chatbots handling financial questions or algorithms flagging credit card fraud.
“Financial institutions around the world are making large-scale investments in AI, while governments and regulators seek to grapple with the significant uncertainties and growing public trepidation as AI becomes central to the fabric of institutions and markets,” according to a recent report from The World Economic Forum (WEF).
AI Will Change the Customer Experience
We might not see bank tellers replaced by humanoid robots in our lifetime, but the experience for customers in the financial services sector is set to transform in more subtle ways.
Already, AI is reshaping wealth management through mobile banking offerings and robo-advisors like those at Toronto-based firm Wealthsimple, writes Joel Schlesinger in the Globe and Mail.
“The company was launched in 2014 by tech wunderkind Michael Katchen, a millennial who made his fortune in Silicon Valley,” he adds. “It has since won over more than 30,000 investors with $1-billion in assets under management.”
And there’s a rising number of increasingly-popular apps now offering similarly personalized financial advice, underpinned by AI.
“These intelligent systems track income, essential recurring expenses, and spending habits and come up with an optimized plan and financial tips,” according to Arthur Bachinskiy, COO of Delaware-based business solutions firm Django Stars.
The big change for customers? All of it is now a click away, with no in-person personal advisor needed.
“AI can deliver a radically reimagined customer experience by allowing customers’ finances to run themselves, and acting as a trusted adviser in moments of need,” according to the WEF.
The Decision-Making Process Will Get an Overhaul
Be it navigating potential risks or assessing credit ratings, AI offers a new way to streamline financial decision-making.
“It’s difficult to overestimate the impact of AI in financial services when it comes to risk management,” Bachinskiy writes.
That’s because the big processing power of cognitive computing allows vast amounts of data to be handled in a short time, he adds — quickly identifying early warning signs and revolutionizing a task that would take humans far longer.
Digital banks are also using machine learning to evaluate loan eligibility, Bachinskiy continues. The process is faster, more accurate, costs less, and yet potentially more complex and comprehensive than traditional credit scoring systems.
“Objectivity is another benefit of the AI-powered mechanism,” he adds. “Unlike a human being, a machine is not likely to be biased.”
Smarter decision-making will also come in the form of using advanced data science to optimize business outcomes — like obtaining lower default rates — and gain insights across business units to achieve better capital allocation, the WEF notes.
AI Will Improve Fraud-Prevention Efforts
In a recent report, security firm McAfee estimates that cybercrime is costing the world economy around $600 billion, or 0.8 percent of global gross domestic product.
But right now, fraud prevention efforts are being run inefficiently and ineffectively, according to the WEF — something that’s thankfully set to change as AI grows more common in the financial services sphere.
“For a number of years now, artificial intelligence has been very successful in battling financial fraud — and the future is looking brighter every year, as machine learning is catching up with the criminals,” Bachinskiy writes.
It’s particularly good at preventing credit card fraud, he adds, which is on the rise thanks to the growing popularity of online shopping. “Fraud detection systems analyze clients’ behavior, location, and buying habits and trigger a security mechanism when something seems out of order and contradicts the established spending pattern.”
In traditional systems, false positives are too common as the frameworks flag anything outside a given set of parameters, notes Vian Chinner in Forbes.
“For example, if you are planning a trip abroad and you start buying airline tickets and accommodation, this may trigger a fraud warning,” he continues.
But a smarter system could use your full customer data to paint a broader picture and compare you to other similar users — like holiday travelers, for instance — before raising a fraud flag on your account, Chinner explains.
It’s Going to Transform Trading
What’s a more apt use of AI than crunching numbers for an optimal result?
“Data-driven investments have been rising steadily over the last five years and closed in on a trillion dollars in 2018,” Bachinskiy writes. “This kind of trading has been expanding rapidly across the world’s stock markets, and for good reason: artificial intelligence offers multiple significant benefits.”
Benefits like increased accuracy, he says, thanks to the rapid algorithm-based analysis of vast troves of past data — leading to faster processing, faster decisions, and in turn, faster transactions.
Taiwanese AI trading startup HiHedge puts it this way: Their software can recognize trading patterns that are undetectable by humans, including price and volume from exchanges around the world, global news in multiple languages, macroeconomic and company accounting data, and more.
“Machine learning, especially deep reinforcement learning, unfolds the potentials of computers to make unbiased decisions,” says the company’s CEO and founder Chiachi Ku. “This is what the future promises.”