The changing role of AI in financial markets

Darren Sinden

The sell-side is seen as being likely to scale their investments in these technologies more quickly than their buy-side counterparts

Artificial intelligence and machine learning techniques are technologies that have the power to transform businesses and their marketplaces. Identifying what form those transformations will take and how businesses within the Financial markets can position themselves to take advantage of them is no easy task.

However, a new report from Refinitiv manages to shed some light on just that subject. The rise of the data scientist as the report is titled is the second annual report on the sector from the financial data and analytics group.

The report surveyed more than 420 participants at commercial and investment banks, broker-dealers, exchanges, hedge funds, asset managers and venture capital firms. Those surveyed held roles that ranged from data science, quantitative development and analysis, to the C-suite.

Geographically the respondents were split between Asia Pacific, Europe and the Americas. With approximately one-third of participants located in each region. Though there was a slight numerical bias towards Asia Pacific which had 7 more participants than Europe and 9 more than the Americas.

The key findings of the report were that firms are now tending to scale their AI and machine learning capabilities across multiple areas of their businesses.

As these operations mature the recruitment of data scientists increases and the role of AI and machine learning moves from being a supportive one. perhaps assessing the effectiveness of marketing campaigns, to proactive roles which help to drive strategy.

Techniques such as natural language processing can unlock the significant value hidden in the unstructured data sets that many firms possess.

However, data quality and availability are now seen as the biggest barrier to the wider adoption of AI and machine learning. As in the words of the report, talent, technology and funding issues fade away.

AI and machine learning are not a panacea however and a firms AI models will only be as effective as their data strategy allows.

The fallout from COVID-19 is likely to boost investment in AI by firms with existing programs. However, access to greater quantities of alternative data will be required for these technologies to be effective against or ready for other black swan events.

In terms of specific responses from those surveyed 72% said that AI and or machine learning is a core component of their business strategy. Whilst 80% said they are making a significant investment in these technologies. 70% of respondents agreed that decision making about their AI and machine learning strategy is taken across multiple areas of their businesses.

The number of use cases for AI is also expanding, Risk management and trading are the two most prevalent uses with more than 60% of organisations surveying using AI in these functions.

However, reporting and compliance scored well at 33%. AI is also being used in research and idea generation among 31% of the businesses surveyed. Other popular use cases included customer servicing and targeting, pre-trade analytics and customer onboarding.

The sell-side is seen as being likely to scale their investments in these technologies more quickly than their buy-side counterparts and is more likely to have deployed the technology across multiple areas of their businesses. The buy-side has so far primarily deployed the technology in pockets rather than business-wide.

Some 44 per cent of sell-side firms had a wide-ranging deployment compared to just 28% of buy-side organisations.

65% of firms surveyed use one type of AI and machine learning, 28% use two. However, only 7% of firms deploy 3 types of this technology in their operations.

Finally in terms of applications and areas of interest firms in the financial markets are increasingly looking at deep learning. Some 75% of respondents said that were using these applications though here it is the buy-side that is leading the process.

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