This is a great article I discovered on Marketwatch.
Like factory work before it, Wall Street jobs are increasingly automated
President Donald Trump has vowed to bring manufacturing jobs back to the U.S. through new policies and regulatory reform. But this effort faces a strong headwind: In all walks of life, human employment is being challenged.
Many manufacturing jobs have been replaced by robots. Meanwhile, drivers are on their way to being displaced by driverless cars, tax professionals by software, and much more.
Recently Trump turned his attention to the financial services industry, signing two directives aimed at repealing portions of the Dodd-Frank and Consumer Protection acts, citing onerous restrictions that hamper legitimate investing and financial activity.
But regulatory change isn’t likely to repel the march of the robots that is transforming the financial services business. FinTech — the finance industry equivalent of robots in manufacturing — is too far along for that. If future investors and consumers of financial services begin to trust FinTech platforms as they have done in retail and travel, then fewer humans will be working in finance.
The raw materials of finance are capital and trust. We trust banks to hold our money and give it back to us when we want it; and we trust brokerage firms to buy the securities we want at market prices and to debit and credit our accounts accordingly. How can robots replace humans in a trust intensive business?
The answer lies largely in the emergence of a fundamentally new business model: the “platform” business. In a platform transaction, processes and checks and balances are done securely by machines.
More importantly, customers’ wide acceptance of (and participation in) a platform business itself facilitates trust. Consider: Potential customers witness the acceptance and participation of their peers, eventually becoming comfortable enough to participate themselves. Such comfort with platforms extends broadly across industries and organizations. Indeed, trust in a platform appears to depend much more heavily on the platform and brand than on the type of business the platform provides.
1. Open access: Anyone with internet connectivity can participate.
2. Functionality embedded in an IT system: Automation enables customers to conduct important operations (for example: search: route; optimize, analyze).
3. Key business processes: These are enabled by the technology for business-specific activities. Amazon is a good example of such a platform, allowing anyone with network connectivity to access its complex and highly automated sales, inventory, and order fulfillment processes.
Similar platforms are appearing in finance. So called robo advisers such as Betterment and peer-to-peer lending services such as Lending Club are early examples. Robo advisers allow customers to conduct investment activities such as trading and portfolio rebalancing according to client objectives. Such platforms are still rudimentary and lack many basic human capabilities such as interpreting reports and understanding the implications of policy changes and regulation. Similarly, peer-to-peer lending platforms are in their early days and being watched by regulators for unforeseen risks they could impose on borrowers and lenders..
Despite these limitations, FinTech platforms are on the rise. Robo adviser platforms do many core investment and risk-management activities quite well, and this seems to be more than adequate for large numbers of retail investors. Peer-to-peer lending is meeting a key need and is likely to continue to grow. Specialized platforms for specific business processes such as payables, payments, and compliance (or “RegTech”) are also emerging. The emergence of these FinTech platforms will be impacted by regulation, especially as the lines between finance and technology blur and regulators need to determine whether to treat these platforms as banks or technology companies.
At the same time, developments in machine intelligence, learning and natural language processing are enabling computers to interpret text and unstructured inputs directly from real-world sources. This will continue to extend the capabilities of FinTech platforms. The Robo adviser platforms of the future will not only balance clients’ portfolios but may well be able to synthesize real-world developments and their implications into portfolio strategies.
Regardless of the outcome of Trump’s attempts to roll back regulations enacted in the aftermath of the financial crisis, automation through FinTech is a clear trend. Partnerships between technology platforms and financial services franchises are already being formed. For example, during Super Bowl LI in February, a commercial for H&R Block’s HRB, +0.68% partnership with IBM’s IBM, -0.44% Watson for preparing tax returns was inadvertently juxtaposed against one for Google’s constantly connected AI- and big-data-driven “home” device.
Just as H&R Block chose not to build its own Watson from scratch, other financial players are unlikely to choose to build Google’s or Amazon’s artificial intelligence and machine-learning capabilities from scratch. Platform partnerships seem much more probable, and both the current administration and regulators are unlikely to prevent a future where your financial adviser or intermediary could be a machine.
Vasant Dhar is professor at the Stern School of Business and the Center for Data Science at NYU and chief editor of the Big Data journal. Roger M. Stein is a research affiliate at the MIT Sloan School of Management in the Laboratory for Financial Engineering. He is also an adjunct professor at NYU’s Stern School of Business.
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