Dad, are you better than a robot?

In London this week, many young people who are leaving school have been doing their work experience.

Exams finished, their enthusiasm, momentum and keen intellect are being turned to real-world problems. Hence the discussion I witnessed on a train leaving the City the other day.

A lad of 16 or more years turned to his dad and said: “I am amazed at the amount of duplication of work that goes on at your firm.”

His dad looked back with an enquiring eyebrow.

“I would have thought that things would have been a lot more automated,” the young man continued. “Surely your bank has people who could sort this out?”

The dad turned and said: “While things are this way, I have a job.”

For many workers on Wall Street and in London’s City/Canary Wharf, the idea that a machine could lead to redundancy is a real psychological barrier to adopting smart, decision-making technology. In some areas, people will see their replacement by robots as a real and present danger, not a future scenario.

Trading desks have long been targets of automation. Algorithmic trading systems have allowed the more mundane trading patterns to be run by machines, enabling workloads to increase without a corresponding increase in headcount. At the extreme ends it has also led to completely new quantitative investing and trading models, based on datasets too large and at speeds too fast for a human to process.

The high point of automation has been to handle routine tasks, according to an academic paper “Jobless Recoveries.” It found that the percentage of jobs involving routine work since 1988 had fallen, while the percentage of jobs involving non-routine work had increased over the same period.

 “To date automation has, in the round, led to the creation of new jobs and improved living standards,” wrote Adam Corlett of think-tank Resolution Foundation in his July 2016 report, “Robot Wars: Automation and the labor market.”

Machines are not managing businesses; they are making businesses more manageable. The next stage of evolution is to use machine learning to enhance the non-routine work that humans do, so that each worker becomes more accurate, more productive and faster.

As the recent need for modelling negative interest rates has proven, a system – even one with the capacity to learn – may find its limits tested as economic realities change. So while workers may fear replacement by machines, the fact is that business creates new opportunities which allow knowledgeable workers to become better than they have been at their jobs.

What the father on the train should have said to his son was: “a machine is better than a human, but a human with a machine is better than a machine.”

Our understanding of risk, of liquidity, of navigating market structure has been greatly enhanced by the application of smart systems. However, it is incumbent upon senior management that they lay the groundwork for their workers to understand how they will fit into the enterprise in the future - and how they will have their own roles enhanced by this evolution.