Refinitiv: Machine learning in finance hindered by data

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By Pia Hecher.

Refinitiv, the financial markets data and infrastructure provider, found that 90% of financial firms it surveyed deploy machine learning (ML) and 75% are investing significantly in the technology.

Refinitiv reported that financial firms are either using ML as a main part of their business (46%) or in pockets (44%). The firm surveyed c-level executives and data scientists in December 2018, interviewing 170 respondents from Asia-Pacific, 161 from Europe and 116 from North America.

Tim Baker
Tim Baker, global head of applied innovation, Refinitiv

“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, Refinitiv’s global head of applied innovation.

Refinitiv stated that financial firms predominantly use ML for risk management (82% of respondents), performance analytics and reporting (74%) and alpha generation (63%). They adopt ML in order to obtain higher quality information (60%), heighten productivity and speed (48%), and lower costs (46%).

“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,” Baker added.

Refinitiv claims its survey illustrates the industry’s progress since 2017, when research technology companies were the main users of artificial intelligence (AI) and only 28% of financial firms adopted AI. In the future, 62% of c-suite respondents expect to hire more data scientists.

Obstacles remain, however, as respondents told Refinitiv that poor-quality data hindered them from making the most of AI and ML. 43% mentioned poor-quality data as the largest barrier to adoption, while 38% indicated a deficit of data availability (38%). A third of respondents ranked challenges around data quality ahead of access to talent.

©TheDESK 2018

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