AI Emerges as Key Focus for the Buy-Side, Says SIX

Three years ago when Swiss financial data and market infrastructure provider SIX launched its first report together with Crisil Coalition Greenwich on the state of play within the buy-side, the subject of artificial intelligence barely made an appearance.

The findings suggest there has been a remarkable rise in the level of maturity within the space, with fears abating that AI is little more than window dressing threatening another dot-com bubble disaster.

“In all technological areas, banks or asset managers have always been slower but what I’m seeing through the results from this report specifically is the respondents… have told us that they do see a return on investment and they do see interest,” Matthew Nurse, head market data, financial information at SIX, told Data Management Insight. “AI is more than just a marketing fad.”

New Trends

It’s on the theme of AI deployment that Nurse found some of the most remarkable changes, especially concerning ROI from AI applications. At the start of the year, anecdotal evidence suggested that many companies that had tried AI couldn’t make it pay and so were taking a more circumspect approach to adoption.

The problem, according to comments from some industry leaders, was a lack data of the right quality to ensure valuable outcomes from their early experiments with AI. Surveys also suggested that the complexity of the data required to properly use AI was proving beyond the capabilities of many adopters. Asset managers, in particular, were seen struggling to roll out the technology.

New Horizon

But the study by SIX and Crisil Coalition Greenwich suggests that a tipping point has been reached.

“This report told me that there is actually some genuine, tangible use going on,” he said. “But it will be progressive. It’s not a big bang thing, but I think eventually it will normalise down to being another part of the toolkit, and there’ll be a lot of internal use.”

The application of AI to data management and value-mining from internal data has emerged as key development in Nurse’s investigations. New AI and machine learning software is enabling firms to better locate and use data stored within their own systems and that they may have been unaware was even there, he said.

Data vendors are responding in kind, too, he said. Some are redrawing their licensing models to accommodate use of the data they supply in clients’ AI applications.

Banks, in particular, have made strides in their use of AI, he said. As well as embedding the technology into client and adviser applications that let them access, query and analyse data for financial management purposes, he sees many institutions using the large language models for their own operations.

Product Development

Among the nascent AI technologies being adopted are portfolio research tools that offer performance and allocation analytics. However, he doesn’t yet see institutions adopting AI models to manage portfolios.

Nurse said that SIX is investing in helping its clients make full use of AI, through the standardisation of its own data sets that it feeds to institutions, and also in making data available in real time.

Fears that AI would take jobs haven’t materialised, he said. Instead it has evolved to help teams work better and Nurse sees that same happening in the management of data. Processes such as data quality checking and content anomaly checking are ripe for AI automation as part of a shifting focus towards using the technology to grease the wheels of institutions’ operations.

“The natural inclination for business is to go to the revenue generation part,” he said. “But I think that considering the total cost of ownership of data – looking at how you manage your data internally and how you manage and use technology internally – is important.”