Written By: Matthew Hemmings – International Director
Just recently, Goldman Sachs reduced the number of employees on one of its major trading desks from 500 a few years ago to merely three. The bank is making huge shifts towards more investment in technology and reducing its headcount, with the goal of “increasing efficiency”. But it is not the only one, as this seems to be a larger trend. Apple, for example, bought Lattice.io last year to help it in analyzing data by structuring it. Google also is among the leaders in the field and has shifted focus from a mobile-first worldto an AI-first world.
Many companies and even entire advanced economies are now shifting towards more reliance on increasingly intelligent machines to do the jobs that humans once did. This comes, of course, with resistance from people whose livelihoods are at stake.
Countries with highest displacement rates
According to McKinsey & Company, the rate of displacement depends on a variety of factors. First, the country’s income level. Second, the demographics. And third, the industry structure. China and India are the two countries with the highest rate. They are followed by the United States, Japan and Germany. Different demographic structures lead to different demand structures, and thus technological progress is likely to take place in sectors with higher demand. And last, the industry structure influences the rate of displacement depending on the type of skills required.
The rise of algorithmic trading
In the financial sector, computers seem to be handling more and more tasks. More sophisticated machines with advanced modes of “thinking” are capable of handling more and more data to make investment decisions, given their enormous computational power. In the year 2013, algorithmic trading was responsible for only 15 percent of market transactions. In 2012, it was responsible for 85 percent. Today, it is expected to be even higher.
The market for algorithmic trading is also growing alongside this trend. Studies say that in the period from 2016 to 2020, algorithmic trading is expected to grow at 10.3 percent CAGR (compound annual growth rate).
Algorithmic trading offers many advantages to its users. Mainly, it offers strategies that can be devised and executed with very high precision. Owing to that precision, it offers high portfolio performance that can exceed 100 percent per annum, given that the accuracy of algorithmic strategies often reachesand exceeds 90 percent. This all takes a great toll off of the analysts’ shoulders, which explains the high demand.
But the main concern is that this automation does not only help analystsbut renders them useless. This concern is justified in the short-term, but in the long run the picture is a different one.
Over the long run, technological progress generates more jobs.
The phenomenon of workers’ displacement by machines is a multifaceted one. Multiple societal, economic and technological factors are at play. Adoption of new technologies is the main factor that could influence this displacement, but the effect is a short-term one. The benefits of those technologies need to be factored into the equation as increasing adoption would lead to increased efficiencies and higher growth, which in turn leads to more demand for work.
Demand is shifting to different types of skills.
The difficult phase remains the transition phase, during which many jobs are shed and incomes are affected. During such a stage, the toll lies on the workers to develop their skills to a higher level and on governments to offer the appropriate infrastructure for such development. The skills that are expected to have high demand in the future include emotional intelligence, creative thinking and talent that is hard to be copied by machines. There will likely be less demand for technical skills, and one scenario suggests that by the year 2030, around 3 to 14 percent of global workers (75 million to 375 million) will be required to change their occupations, aside from developing the skills required to use increasingly sophisticated machines.
This applies to finance careers just as equally as any other type of career. Finance professionals, such as insurance analysts, for example, are at risk of being replaced by artificial intelligence that can predict life expectancy with more accuracy, thus reducing costs incurred by insurance companies significantly. But this does not mean that humans have become dispensable yet. Human functions such as empathy, compassion, strategic thinking and sometimes even common sense remain beyond the capabilities of machines. Most roles will still need to be filled by humans, while machines will make their work easier and more efficient.
Conclusion
Technological progress is inevitable, and it has been a benign force throughout history. The pace of job losses to machines is less alarming than commonly believed. The key challenge lies not in job displacement by more capable machines but in enabling educational institutions and job-training systems to adapt tothe changes. Over the long-term, more advanced technology spurs economic development and growth, which in turn increases demand for work, generating more job opportunities. This usually more than offsets the earlier job loss. Historic accounts support such a view. Markets are dynamic and are constantly evolving into more efficient ones, and the pressure is on the workforce to remain nimble in the face of such evolution.