In our previous post, we discussed some of the factors driving the increasing automation of jobs. As you’ll recall, automation follows a general rule: those jobs which demand physical, predictable, and rote work have a higher likelihood of being automated.
Believe it or not, Wall Street is one industry where jobs are threatened by automation. There are two reasons for this: first, a considerable portion of investment work is surprisingly repetitive, (and thus easily automated); second, AIs and financial technology (fintech) startups are quickly becoming increasingly sophisticated, predictive programs.
Read on to learn about two areas of finance where robots are taking over—and one way that humans are pushing back.
The plight of the analyst
Part of the push to automate jobs in finance and investment banking stems from the exorbitant salaries paid to even the most junior of employees. First-year analysts at a major bank can expect to earn $70-100,000 annually before signing bonuses and incentive pay, while associates (the next step up from analyst) can easily take home $150-200,000 per year.
With such high pay for even lower-level positions, it’s no surprise that such roles are first on the chopping block. Given that nearly half of their workday (43%) is spent collecting and processing data, companies are already beginning to look at software that will take over many of these functions.
In fact, there are a host of startups that will soon make analysts obsolete, from Kensho, whose proprietary program analyzes raw data and generates market reports and stock forecasts, to Skytree, an adaptive AI that automates investment predictions and applies machine learning to the stock market. To give you an idea of Kensho’s processing power, it takes the program a few minutes to run an extensive analysis on how the Syrian civil war will affect global oil prices—something that takes highly-paid human analysts a week to accomplish.
At the very least, the responsibilities of a lower-ranking financial analyst or associate will be altered significantly. Even if they are not automated outright, it’s probably the case that analysts and associates will see their duties shift from data processing and prediction to higher-level work, such as designing financial instruments or convincing customers.
But analysts aren’t the only ones in the finance industry who are marked for change. Even traders, those omnipresent personalities who once dominated the floors of stock exchanges, have largely been phased out. By one count, there were over 5,500 floor traders in the NYSE in 2000; sixteen years later, there are fewer than 400.
So what caused the floor trader, the icon of the stock market, to decline so dramatically?
Computers, specifically algorithmic trading, which form the backbone of stock markets today. Because computers are many times faster than humans, they can easily be programmed to rapidly make a high volume of trades—so long as these trades stay within a certain set of parameters, which take into account factors like timing, price, quantity, and other mathematical models.
Perhaps the greatest advantage of algorithmic trading is that, by trading many smaller transactions at a higher rate (rather than trading fewer, large transactions), algorithms help investors maintain a liquid market—one that would be impossible if it were run by humans, whose flesh-based synapses are considerably slower. Additionally, because algorithms aren’t paid the hefty commissions of otherwise human brokers and traders, they are much cheaper to operate.
Yet there are problems with these new technologies as well. As Wired points out, while algorithms are an indispensable part of today’s stock markets, from assessing and trading individual stocks to executing massive trades as quickly and quietly as possible (in order to avoid outside manipulation), these technologies have sparked an arms race, as AIs face off, one side trying to protect their trades and secrets as their opponents try to crack those transactions open.
More importantly, faster markets with more liquidity aren’t necessarily safer ones. By trading so many stocks so quickly, investors run the risk of falling being overwhelmed by their programs—especially dangerous when it comes to technical glitches. In the most famous instance of algorithms gone bad, Knight Capital lost $440 million in a half hour; the culprit was a rogue program slated for deletion, which bought high and sold low (the opposite of its programming), and nearly bankrupted the firm with a high volume of faulty trades.
How Wall Street is pushing back
But possible market meltdowns aside, the pending, human costs of this automation push will be high. Take the example of State Street, a bank founded shortly after America won its independence from Britain; due to increasing automation, this venerable institution is predicted to cut nearly 20% of its 32,000 strong workforce by 2020. In one study by Citigroup, one CEO predicted that within ten years, 50% of employees in finance will lose their jobs to automation.
But humans are fighting back. In a rare instance of humans winning out over machines, Bloomberg reports that, when it comes to sales trading (larger blocks of stock), humans have the edge: from long experience, human sales traders can be more cost-effective, intuitive, and networked than their machine counterparts—as they can interact with other humans in ways that AIs can’t (yet) match.
But human sales traders have a specialized skillset: they deal primarily with larger clients on massive trades, where personal trust and business relationships are paramount. In that sense then, sales traders are similar to travel agents, who faced existential competition from startups like Kayak and Expedia. Like sales traders, travel agents narrowed their focus, pivoting to luxury, upmarket niches to avoid going out of business entirely.
In much the same way, Wall Street analysts and associates will have to pivot if they wish to stay in business. Given the current, cost-cutting mindsets of big business, along with the advent of increasingly capable automation, the future for the vast majority of lower- and mid-level investment professionals is bleak indeed.