Home Business NewsBusiness Financial sector quants and data analysts held back by lack of automation

Financial sector quants and data analysts held back by lack of automation

by LLB Editor
26th Jan 21 11:33 am

Fewer than four-in-ten (37%) of data scientists in financial services firms currently use AI, machine learning and other advanced technologies in their key analysis and investment processes and workflows, according to new research executed in the UK, US and Asia, for Alveo a leading solutions provider of managed data services for data mastering and analytics.

Conducted among banks, investment companies, insurance firms and hedge funds, the research reveals how the slow adoption of AI and other cutting-edge automation technology is seriously hindering quants and data analysts in their most valuable work.

Two-thirds (66%) of respondents say quants and data analysts in their organisation have to spend between 25% and 50% of their time collecting, preparing and quality-controlling data; time they could otherwise have spent on modelling and analysis.

Poor data quality also prevents risk managers from making the best use of analytics. Nearly one-in-three respondents (29%) say problems with data quality are most severe in risk management and market making.

The benefits of data integration are, however, appreciated by more than a quarter of respondents. 27% agree that improved productivity is one of the main gains from more closely integrating market data and reference data into advanced data analytics – a task vastly accelerated through integration of data using AI and machine learning.

“If financial services firms are to harness the power of analytics they must develop an integrated approach to managing and provisioning data,” says Mark Hepsworth, CEO, Alveo. “This will require AI, machine learning and related technologies to prepare the right data. Highly skilled quants and data analysts should not be held back by having to spend hours improving poor quality data when the technologies are there to complete the task for them.”

Despite problems with data quality, risk management is the area where analytics are most commonly used. 44% of respondents say risk management departments/units make extensive use of data analytics within their organisation today, ahead of finance (37%) and operations (36%).

Discovery of risk factors is also one of the main reasons AI is used by financial services organisations – cited by 25% of respondents. Risk management is leading the way in many organisations, with other departments needing to catch up.

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