A new Artificial Intelligence/Machine Learning survey launched by Refinitiv has found that while the use of these techniques is pervasive in the financial community, poor quality data impedes their ability to leverage the advantages.
The survey found that 90% of the c-level executives and data scientists surveyed have already deployed machine learning, while all of the c-level participants said it is core to their business strategy.
On the downside, 43% cite poor quality data as the biggest barrier to adoption followed by a lack of data availability (38%). Despite being a technology area that has seen an increasing demand for a relative thin talent pool, the challenges around data quality were ranked ahead of access to talent, which was highlighted by a third of the respondents.
The survey involved in-depth interviews of nearly 450 financial professionals across North America, Europe and Asia and Refinitiv says its findings confirm how far the industry has evolved since 2017 when research indicated technology companies were the primary adopters of AI and only 28% of financial-services firms were deploying it.
Other key findings from the 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. Equally, 75% of firms are making significant investments in machine learning and 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,” says 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.
“We see a future of accelerating innovation fuelled by wider availability of powerful cloud-based artificial intelligence and machine learning tools dramatically lowering entry barriers and thus changing the competitive dynamic across the industry,” he continues. “But no financial institution will be able to use the technology successfully unless the underlying data is machine ready.”