Cognitive technologies are transforming capital markets. Once the preserve of IT experts, they are now moving to center stage—offering enhanced speed, accuracy, and efficiency, and creating 20 to 30 percent in additional capacity, as employees in areas such as post-trade processing are freed from automatable tasks to focus on higher-value activities. The challenge for market participants, facing an array of solutions, is to implement at scale and capture the maximum value at the lowest possible risk.
Cognitive technologies are applications and machines that perform tasks that previously required human intelligence. They include robotic process automation (RPA), machine learning, and natural language processing, which reduce the need for human input and increase effectiveness through new insights and ways of working. Many of the technologies operate at the frontier of the technologically possible, but as the price of hardware falls the case for their use gets stronger.
Already there are numerous examples of cognitive technologies in action. In the trade life cycle there are at least five either ready to roll out (with commercial solutions in place) or being piloted across the industry:
RPA: automation of routine tasks through existing interfaces, used for activities including data extraction and cleaning
Smart work flows: routing and integration of tasks such as client on-boarding and month-end reporting (usually in combination with RPA)
Machine learning: application of advanced algorithms to large data sets to identify patterns, helping make decisions in areas such as idea presentment (CRM), product control, and trade surveillance
Natural language processing: turning speech and text including legal documentation and client-service queries into structured, searchable data
Cognitive agents: computerized interaction with humans, used, for example, in employee service centers, on help desks and in other internal contact centers
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McKinsey recently compared the penetration of digital execution and trade processing of eight banks in the cash equities business, and found that those with the highest levels of digital execution saw front-office revenues per producer increase by as much as eight times, while those with the highest level of post-trade digitization posted four times more trades per middle- and back-office FTE than the bank with the weakest digital resources (exhibit).
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Often the efficiency impact of technology does not correlate directly with headcount reductions. This is because automation applies to tasks rather than positions. For example, RPA typically reduces an individual’s workload by 10 to 20 percent. Still, viewed through the lens of overall capacity creation, the impact of cognitive technologies is potentially significant. In the middle office, it may ,for example, generate additional capacity of around 25 percent, while in an operations function, capacity can be increased by as much as a third.
Cognitive technologies can be useful as a stand-alone solution, but the impact is multiplied when applications are operated in combination. For example, RPA and machine learning have high utility for the correction of standard instructions in the settlement and payments function. Meanwhile, cognitive agents, smart work flows, and natural language processing are most useful in client services, for example, for creating customized email responses.