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Refinitiv Reviews Potential of Machine Learning and Problems of Data Quality

Machine learning and artificial intelligence (AI) have moved beyond the experimental stage to become core components of business strategy and investment. Key use cases include risk management, performance analysis and trading idea generation, ahead of automation and cutting costs. The whole is driven by growing numbers of data scientists employed by financial organisations.

The barriers to adoption and implementation, as in so many cases, include poor data quality and availability, incomplete records, lack of capacity to manage the size and frequency of data, and cleaning and normalising the data. A lack of funding can also stymie adoption. Further difficulties include a mismatch between the boardroom and data scientists, with C-level professionals believing it is important to be seen using the latest tools for competitive advantage, and data scientists under pressure to deliver on the promise of machine learning while facing organisational constraints at ground level.

These are some of the headlines from research into AI and machine learning by Refinitiv that was published this week in the first of a series of annual reports on technology innovation, and led the company to suggest that AI will be the single greatest enabler of competitive advantage in the financial services sector.

Considering some of the key results of the research, Amanda West, head of innovation enablement in Refinitiv’s applied innovation team, comments: “The importance of getting to grips with data quality and helping solve this for customers is not surprising. What we hadn’t anticipated was the rate and level of operational adoption of machine learning shown by the research, and a primary use case of risk management rather than expected cost take out.”

Other results from the research show the buy-side leading the sell-side in making machine learning part of business strategy, alternative data and unstructured data becoming significant subjects of machine learning alongside market and internal company data, and data scientists being predominantly consolidated into a few teams rather than being distributed across a number of teams.

From a geographic perspective, financial services professionals in North America are more advanced than those in Europe and Asia as a result of the largest financial services organisations being headquartered in the region, initial innovation coming largely from local universities, and the financial market being more homogeneous than in the rest of the world. That said, Refinitiv suggests these reasons behind North American leadership are eroding.

The research for the Refinitiv report was carried out by Coleman Research in December 2018 and included 447 telephone interviews with data science practitioners and C-level data science decision makers in financial institutions with annual revenue of more than $1 billion.

The survey was global and included only participants from organisations that were using machine learning (98% of participants) or intending to do so in future (2%).

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