A report by consultancy Opimas predicts major job shifts for the financial sector with asset managers, in particular, losing 90,000 jobs to artificial intelligence and machine learning technologies.
While the benefits of artificial intelligence (AI) have been clearly drawn, it will come at a cost in terms of job losses.
By 2025, financial institutions could see 230,000 jobs disappear and be replaced by robotic process automation, machine and deep learning or cognitive analytics, according to a new report, “Artificial Intelligence in Capital Markets: The Next Operational Revolution,” from consultancy Opimas, based near Paris, France.
The report’s authors note that the asset management industry would be the worst hit, with the loss of around 90,000 positions as their clients increasingly sought cheaper and automated strategies over the more traditional offerings.
On the flipside, there will be 30,000 new roles created but they will be slated toward the AI technology and data providers who are nimble enough to respond to the new demands for AI implementations. Opimas also notes that financial institutions should yield a 28 percent improvement in their cost-to-income ratio by 2025 as they automate routine processes currently performed by employees.
These cost savings and greater operational efficiencies explain why investment in AI technologies will continue on an upward trajectory over the next four years. Optimas forecasts a roughly 75 percent increase in expenditure from the current $1.5 billion to $2.8 billion in 2021. The figure does not include merger and acquisition activity and investments in startups.
Drilling down into the different technologies, robotic process automation (RPA), which aims to replace manual handling of automated processes for repetitive and high-volume tasks, will account for a significant chunk in the short run, although spending is expected to peak at $441.6 million in 2018, as industry players reap the benefits of investment.
The report also stresses that the future belongs to cognitive analytics and machine learning solutions. They will represent the lion’s share of investment over the next five years with financial institutions increasing their investments in these technologies from $576 million for cognitive analytics and $640 million for machine learning, in 2017 to nearly to $1 billion for both in 2021.
A new and separate report, “The Start-up View: a Year in FinTech,” from the Startupbootcamp FinTech London program and PwC, shows that these fledgling companies are also getting in on the action and focusing most of their attention on using AI to solve customer problems.
Last year, the report reveals that around 16 percent or one in seven of the applications received by the program was aimed at building new prototypes, many of which were based on AI and machine learning. By contrast, blockchain, which has been the darling of the fintech world, saw a decrease to 6 percent of applications as there have already been several initiatives in this space.
“There has been a lot of attention on blockchain, but I think it is over-represented in the fintech industry,” says Axel Pierron, industry veteran and managing director of Opimas. “The advantage of AI is that firms can pursue their own strategies to improve their processes and do not have to rely on or collaborate with other market participants,” Pierron says.
Pierron adds that another difference is that unlike blockchain, which still has many initiatives in the proof-of-concept phases, AI technology is already implemented in many systems. “One of the biggest challenges for blockchain is to select the right test case in order to get further funding,” he adds. “As for AI, we expect to see the benefits coming through particularly for market participants who need real-time access to new data sources.”
For example, the Optimas report notes that AI can capture structured and unstructured data from a range of sources and leverage that information to improve business decisions. Moreover, it is able to predict, hypothesize and act with other systems autonomously.
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