Use of Machine Learning becomes the New Norm for Financial Community as Sector Builds Smarter Machines to Drive Competitive Advantage
90% of financial firms are using machine learning and all c-suite respondents say it is a core part of their business strategy, but nearly half are being held back by inadequate data, according to new global survey of 450 professionals
April 8, 2019 – Refinitiv announced today new research findings that confirm the use of machine learning is pervasive across the financial community and is critical to its success in the future. Ninety percent 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.
However, respondents 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,’ challenges around data quality were ranked ahead of access to talent, which was highlighted by a third of the respondents.
This information was compiled for the inaugural Artificial Intelligence/Machine Learning (AI/ML) Survey by Refinitiv, featuring in-depth interviews of nearly 450 financial professionals across North America, Europe and Asia. 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.[1]
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
- 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.
“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.”
To learn more about the Refinitiv AI/ML survey, please visit: https://refini.tv/2G8jbnD.
About the survey
The survey was conducted in December 2018 including participants with titles such as chief data officer, chief information officer and chief technology officer from the c-suite, as well as a host of data-scientist titles such as data analyst, data engineer, head of AI, head of data science, head of ML, head of natural language processing and others. The survey was global with 170 respondents from APAC, 161 from Europe and 116 from North America. Both the buy side and sell side were represented.
About Refinitiv
Refinitiv is one of the world’s largest providers of financial markets data and infrastructure, serving over 40,000 institutions in over 190 countries. It provides leading data and insights, trading platforms, and open data and technology platforms that connect a thriving global financial markets community - driving performance in trading, investment, wealth management, regulatory compliance, market data management, enterprise risk and fighting financial crime.
