These Are The Wall Street Jobs Most Threatened By Robots

Cashiers at fast food restaurants aren’t the only workers who should fear being imminently replaced by kiosks and artificial intelligence. Advances in machine-learning software could soon render many high-paying Wall Street jobs obsolete – jobs that will no doubt quickly disappear as electronic trading in equities and foreign exchange markets squeezes trading revenue, forcing banks to seek cost savings elsewhere.

As Bloomberg points out, “the fraternity of bond jockeys, derivatives mavens and stock pickers who've long personified the industry are giving way to algorithms, and soon, artificial intelligence.”

Indeed, firms are already rolling out machine-learning software to recommend trades and hedging strategies. And while many of these tools will undoubtedly help the employees who remain vastly improve productivity (if history is any guide), one day soon, the machines may not need much help.

But as anyone in the industry has probably noticed, banks have stepped up recruiting of tech talent since the financial crisis. Of the jobs Goldman Sachs’s securities business posted online in recent months, most were for tech talent.

Billionaire trader Steven Cohen is reportedly experimenting with automating his top money managers. Venture capitalist Marc Andreessen has said 100,000 financial workers aren’t needed to keep money flowing.

In the future, many of the top jobs on Wall Street won’t be worth the price of a top-tier MBA.

A recent piece published by Bloomberg offers a breakdown of which jobs are most vulnerable to automation – and which positions might outlast the robot invasion. As is often the case in life, there are winners and losers in the Wall Street hierarchy.


Sell-side credit:

The art of dealing in bonds and more bespoke types of credit has proven far more challenging for computers than their much-faster takeover of stock exchanges. Infrequent or opaque trading left humans to negotiate prices, and banks must carefully juggle holdings to minimize the burden on balance sheets. Advancements in natural-language processing, data collection and machine learning are helping to overcome hurdles.

Buy-side credit:

Vast spreadsheets, such as breakdowns of mortgages packed into bonds, are nothing new for credit funds. But some are teaching computers to scan and understand a much larger universe of bond covenants, legal documents and court rulings. Still, fully automating analysis of contract and illiquid assets underpinning securities in opaque markets remains a challenge, for now.

Sell side commodities and securitization:

From highly liquid contracts tied to assets like gold and oil to the physical commodities themselves, the diverse world of commodities doesn't always lend itself to automation. So banks are working on cataloging trader and salesperson conversations to create profiles of clients to help better anticipate their desires.


Sell side rates and foreign exchange:

The long-running shift to electronic currency trading is getting an upgrade. Firms are tapping big data and machine learning to anticipate client demand and price swings. Software also is helping to design and manage banks' inventory of more complex rate swaps and currency derivatives.

Sell side equities:

Equities trading, which shifted decades ago to electronic platforms, is one of the first testing grounds for using artificial intelligence to execute orders.

Buy side equities:

"Hedge funds and asset managers are using predictive analytics for tasks such as timing stock purchases and assessing risk based on market liquidity. Computers are also digesting vast data sets - everything from car registrations to oil-drilling concessions - to help predict how stocks will perform."

Buy side macro:

"Firms are trying to build economists. They're toying with natural-language processing to sift central bank commentary for clues on future monetary policy. They’re also experimenting with algorithms that scour far-flung data, like oil-tanker shipments from the Middle East or satellite images of Chinese industrial sites, to forecast growth."

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Perhaps Elon Musk, who has long expressed what some would describe as paranoid views about the future of automation and artificial intelligence, was speaking in metaphors when he famously quipped that "until people see robots going down the street killing people, they don't know how to react because it seems so ethereal." The robots are coming to destroy jobs that once allowed bank employees to afford the twin luxuries of a suburban home and private school tuition. Given this eventuality, the image of an armed invasion of suburbia is a poignant one indeed.