Globally, respondents also acknowledge that poor-quality data impedes their ability to fully leverage machine learning and artificial-intelligence technology, with 43% citing this as the biggest barrier to adoption followed by a lack of data availability (38%).
Despite being a technology area that’s seen a recent ‘war for talent,’ expected growth in the data science roles associated with machine learning is currently lower in Asia compared to North America, but ahead of EMEA. Overall, however, challenges around data quality were ranked ahead of access to talent, which was highlighted by a third of respondents globally.
This information was compiled for the inaugural Artificial Intelligence/Machine Learning (AI/ML) Survey by Refinitiv, featuring in-depth interviews with nearly 450 financial professionals across Asia (170), Europe (161) and North America (116). Its findings confirm how far the industry has evolved since 2017 research that indicated technology companies were the primary adopters of artificial intelligence (AI) and only 28% of financial-services firms were deploying it.
Other key findings from Refinitiv’s new research include:
90% of financial firms are using machine learning, either in multiple areas as a core part of their business (46%) or in pockets (44%); the 10% of firms that have not yet deployed machine learning are experimenting with it
75% of firms are making significant investments in machine learning
Respondents in Asia are significantly less likely to work with structured data (49%), compared to those in North America (64%)
62% of c-suite respondents plan to hire more data scientists in the future as banks and asset managers seek to give themselves a data and technology edge over competitors
The main applications for using machine learning were in risk use cases (82% of respondents), followed by performance analytics and reporting (74%), with alpha generation in third place (63%)
AI/ML adoption is primarily driven by extracting better quality information (60%), increased productivity and speed (48%), and cost reduction (46%)
“Machine learning and artificial intelligence are often described as emerging technologies, but the fact is they are already being widely applied across financial services,” said Tim Baker, global head of Applied Innovation at Refinitiv. “Whether it is an increasingly complex regulatory environment, the need to find new sources of alpha, or winning the fight against financial crime, the industry is turning to data and technology, and data scientists are increasingly important as the alchemists charged with turning big data into insight.”
“While Asia may currently be behind the Americas and Europe in the maturity of its adoption of machine learning initiatives and the investment put into them, there is clear momentum building behind artificial intelligence applications in the region,” said Sanjna Parasrampuria, Head – Applied Innovation Asia. She added, “The c-suite of all the firms we surveyed view it as a core part of their business strategies, and this will result in them needing access to better quality data, access to world class talent, and powerful cloud-based tools. Efforts in Asia appear particularly focussed in areas of risk mitigation and wealth management.”