Financial services operations run as a blend of “nominal” (what goes according to plan) and “exceptions” (the rest). Any given day includes both flavours and there are days that hit both ends of the spectrum. A career in operations tends to run along this same arc. Junior members of the team typically start with the simplest, lowest exposure processes.
A staffer slowly masters these repetitious tasks, learning how to handle edge cases, the fundamental underlying financial principles, and how to handle when something goes wrong. They move slowly on to more complex exceptions, and then on to improve and define new processes.
For a senior employee, a day’s work in operations has little to do with nominal processing. Their time is saved for the most complex situations, where they can make the most impact. These experts – and most companies only have one or two – are those that have been through several very bad days in operations. They have the accumulated industry, institutional, and technical knowledge to perform the necessary forensics.
Now machines have arrived in operations, and are attempting to climb the very same career curve. Technology is advancing the push to automate operations functions, jumping past simple VBA macros to robotic process automation (RPA) to the use of cognitive learning.
The benefits of this approach are self-evident: a properly deployed piece of technology can lower operational risk while greatly increasing throughput – quickly outpacing even the best junior staffer. The front office benefits by being able to trade more complex products with higher volumes, in turn supporting more complex investment strategies.
The rise of the machines is changing the shape of the operations offices, just as they changed the shape of factory floors in the past century. As machine capabilities grow at the ever increasing rate of technology, more and more entry level functions require fewer and fewer new hires. Human attention is reoriented towards exception handling, and staff can elevate their level of thinking as everyone in the office has more time to refining and reestablishing processes. There is, however, a lower headcount – and overhead – to do that thinking.
While there is general support for automating more in operations, both for commercial and risk benefits, there are long-term risks and impacts to consider. Two major trends stand out as worthy of further discussion:
Risk Exposure: As more and more processing is supported by technology, the impact of severe exceptions and outages also increases. Automation only works when the inputs conform to the expected parameters – if inputs are missing or severely out of sequence, it falls to the staff to do whatever is necessary to ensure the day’s processing is completed. In financial services, the worst case means the shop’s trading is critically impaired and/or trades are made on bad data. The bottom line here is that companies should not build a machine so complicated it can’t be fixed quickly.
Human capital: Automation reduces the demand for those entry-level roles where operations careers are started and domain expertise is developed. Nobody is born as an operations expert, and senior staffers rely on their years of foundational experience to deal with the most complex scenarios. As the need for junior roles shrinks, so does the career path. Shops already tend to over-rely on one to two “experts” A shrinking pool of new talent only exacerbates this condition. Disaster can strike when key resources depart with no real successors in place.
These concerns do not justify a Luddite approach. Smart companies are embracing the benefits of technology in fund operations. They understand the investment in training and process governance that comes with the adoption of more automation. As long as firms take into account the necessary compensating controls to handle the storms – especially in breadth & depth of staff expertise and the key man risk – the rising tide will lift all ships.