• Richard Walker

Who is leading the AI race in Capital Markets?

Updated: Jun 13

A look at some of the most impactful use-cases so far

The capital markets industry is often an early adopter of new technology. Back in the early 2000s firms were experimenting with AI to enable real-time forecasting of financial data. Since then, many new technologies have been developed and implemented across capital markets businesses, but which have had the biggest impact on their business operations?

To help answer this question The Economist Intelligence Unit surveyed more than 200 financial services and capital market professionals worldwide, focusing on their AI use cases. The results are presented in their report below.

Real-time risk management - Highly Likely

An area where AI can be effectively used to improve operational risk management is by applying machine learning models to historical trading data to identify potential market and credit risk. Proactively managing counterparty risk profiles before credit events occur. This theme was rated as having the highest impact by survey respondents, with 94% agreeing that it has or will have a significant impact on their organisation’s market & credit risk management.

Data-driven insights - Likely

Machine learning models can be used to automatically extract actionable insights from large volumes of unstructured data—such as text documents or images—and produce reports that were previously difficult or impossible to analyse using conventional means. This theme was rated as having a moderate level of impact by survey respondents, with 68% agreeing that it has or will have a moderate level of impact on their organisation’s business operations.

More efficient trading strategies - Unlikely

Using machine learning models to develop trading strategies has been a well documented use-case for AI. Driven by the view that machines are able to detect patterns and trends that humans cannot see through traditional methods. However there is much doubt that AI will deliver in this area, with only 18% believing that it has or will have a positive impact on their organisation’s trading activities.

Operational efficiency - Highly Likely

Machine learning models can help reduce costs associated with manual processes such as reconciliation and reporting by identifying and automating tasks that could be completed more quickly and easily using technology—making them more efficient than humans at these tasks. This theme was rated as having a very high level of impact by our survey respondents, with 90% agreeing that it has or will have a very high level of impact on their organisation’s business operations. Much of the off-shored roles that involve repetitive tasks are seen as key candidates for AI.

What are the top three areas for future investment?

The survey also asked our respondents what areas they considered most important for future investment in order for AI technology to have the greatest possible effect within their organisations over the next 3–5 years – given these investments would provide long-term benefits beyond 2022. The top three areas identified were: process automation; data augmentation; and advanced analytics/machine learning techniques (including supervised/unsupervised learning). These themes were seen as offering medium-term benefits leading into 2022 followed by longer-term opportunities over time—with high levels of perceived importance being accorded across all categories.

Longer-term strategic focus: beyond the bottom line

Recent research shows there is little doubt that firms now realise the value proposition offered by AI technologies extends well beyond narrow bottom lines alone—with 88% of firms believing this to be true (or likely) – showing there is now an element of strategy behind choosing which applications drive innovation within an organisation today! As such, firms should continue investing in applications which offer both short-term gains today along with longer-term benefits down the track—as highlighted by the areas where firms believe their business will most change.

While there is no strong consensus firms ranked the change in innovation, the need to upskill the workforce and the development of new products ahead of cost savings.

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