The technology has been deployed by ninety percent of the c-level executives and data scientists surveyed. All c-level participants said machine learning is core to their business strategy, but respondents acknowledged that poor-quality data impedes their ability to fully leverage machine learning and artificial intelligence.
43 percent of participants said poor quality data is the biggest barrier to adoption, followed by a lack of data availability (38%). Access to talent came in third as the biggest barrier.
According to the survey, 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 other 10% are experimenting with it.
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
82% of participants are applying machine learning for risk use cases, 74% in performance analytics and reporting, and 63% in alpha generation. AI/ML adoption is primarily driven by extracting better quality information, according to 60% of respondents. 48% said increased productivity and speed, and 46% chose cost reduction as the main driver of adoption.
Tim Baker, global head of Applied Innovation at Refinitiv, commented: “Machine learning and artificial intelligence are often described as emerging technologies, but the fact is they are already being widely applied across financial services. 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. But no financial institution will be able to use the technology successfully unless the underlying data is machine ready.”
The survey features in-depth interviews of nearly 450 financial professionals across North America, Europe, and Asia.