We’ve always thought of Wall Street as a big boy’s club. The post-frat house frat house, swimming in a sea of stocks and shares, a testosterone bubble in the bloodstream of the world’s economies. But that’s not going to be the case for much longer. The bull pits have been slowly replaced by strings of code, variable functions and, now, robots.
Machine learning is being rolled out to help relieve staff of routine tasks and give them a definitive edge in their combative environment. Being used to suggest bets, set buy and sell prices and to cultivate hedge funds, firms are embracing technology at a febrile rate – scared to be the ones left behind.
But there are those that are going to be left behind. Whilst firms like Goldman Sachs are hiring fin-tech specialists in droves, there are swathes of bankers and businessmen who might soon be out of jobs. Billionaire trader Steven Cohen, for instance, is experimenting with replacing top money managers with automation, and VC Marc Andreessen has stated that ‘100,000 financial workers aren’t needed’ to keep the money flowing.
Looking at what the future of AI means for the future of those in banking, Bloomberg has created a map of trading automation based on interviews with high level execs. It presents a very interesting future for an industry previously so ensconced in the liveries of the city boy. One far more machined, (depending on your view of bankers) far more inhuman.
The following are the main areas that are going to take the hit:
Credit
Dealing in bonds and more bespoke types of credit has proven challenging for computers. Trades in this area are ‘infrequent and opaque’, and banks have to be careful to minimise balance sheet burdens. However, advancements in natural language processing, data collection and machine learning are slowly but surely overcoming these obstacles.
Rates & Exchanges
Companies are using machine learning and big data pools to anticipate client demand and price swings. Software has been created which can not only manage, but design a bank’s inventory of complex rate exchanges and derivatives.
Commodities
According to the report, contracts tied to gold and oil don’t lend themselves to automation. There are many more intricacies to the part than current robots could comprehend, however, software is being used to catalogue trader conversations in order to create client profiles, which will help anticipate their financial desires.
Equities
Predictive analytics are being used by hedge fund and asset managers in order to manage stock purchase times and assess risks. Computers are also being used to scour through incredible amount of related data in order to see how stocks will perform.
Macroeconomics
Rather than employing salesmen and traders, firms are now looking to grow economists in-house. Using natural language processing, they are looking through central bank communications to sniff for clues on future policies. Data from satellites over Chinese industrial sites, and the processes of shipping from the Middle East are being combed through and combined in order to develop forecasts for economic growth.
The end of the banker isn’t quite here. And, in all reality, is probably never going to happen. Whilst we can use robots, machine learning and software to enhance the operations of financial firms, there still needs to be a person on the other side making the big decisions. Pooling the analysis. Being responsible.
Though it does raise some interesting questions on money. If these jobs can eventually all be automated – which is the idea – if it’s just a bunch of computers spewing binary back and forth, where does value really lie?
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