AI tale of two conferences

Oliver Hunt  |  Chief Product Officer  |

I recently attended two Fintech events where Artificial Intelligence unsurprisingly dominated the panels and presentations. Comparisons between these two events reveal some interesting trends.

Unbundling Uncovered, hosted by Substantive Research, is a conference for Investment Research professionals (Buy Side, Sell Side, and innovative vendors in the ecosystem) with a focus on the impact of regulation on research.

FinJS, hosted by OpenFin, meanwhile, is more focused on B2B Fintech itself, and mixes broad panel discussions with highly technical demos.

Here are my three big takeaways:

1. Research insiders are more cautious on GenAI than their colleagues across the rest of Financial Services:

Both conferences were full of excitement for AI, but Unbundling was more cautious on Generative AI than FinJS. This seems like a sensible reflection of some of the unique challenges and value propositions inherent in the Research industry. This is mostly to do with the fact that Investment Research is heavily regulated and expensively produced, and the product is complex, long-form content written by domain experts. My colleague Simon Gregory (Limeglass CTO & Co-Founder) shared some more detailed thoughts on this in a discussion with Substantive Research’s Hamish Risk: Research Discovery – Technology Q&A

At FinJS, by contrast, there was a lot of excitement about GenAI’s role in the world of big data. This includes the growing ability of front-office professionals to engage with datasets in natural language, and for nimble teams to spin up applications with low-code development tools. (Some of the demos on this were amazing!)

2. A new era of co-operation between Research Fintechs should bring welcome efficiencies to the industry:

AI for Research became a more mature concept at Unbundling 2023 than it was a year earlier at Unbundling 2022. Even 12 months ago, concepts like “Research Discovery” were poorly understood and often buried under less meaningful terms like “AI for Research” or “Research NLP”. (For more on Research Discovery, click here).

Firms offering specific services within that “AI for Research” bracket were previously reluctant to focus their message, wanting to pin the AI/NLP label onto their marketing material. Many of those firms whose actual products and use cases were completely different from one another pretended to compete, fearing that there was only room for one “AI for Research” provider.

The reality, of course, is that AI (in all its different forms) will create broad opportunities. In 2023, people now seem to be relaxed about these. As a result, there were plenty of ideas for collaboration being discussed.

As a result, at Limeglass, we think there is cause for optimism in the Investment Research industry. If meaningful partnerships start to emerge between vendors in the research ecosystem, this should lead to simpler, more efficient outcomes for Buy Side and Sell Side alike. Imagine a world with fewer unique logins and fewer separate onboarding processes!

3. Use the GenAI hype dollars for data projects:

There were some Financial Services machine learning veterans at FinJS. They were keen to point out that trading floors and quant funds have been using machine learning for years already.

They cautioned, therefore, that while the latest iterations of Generative AI are significant technological advances, no model will be smart enough to deal with low-quality input data. They suggested, therefore, that the most sensible thing for industry participants to do right now is to take the funding available for GenAI projects and sequester it for data preparation projects instead.

The knowing looks in the room suggested that everyone was all too aware of the poor state of most firms’ datasets!

Overall, two very interesting events, both of which encouraged me about the industry’s direction of travel.

I wonder how things will look at these events in 2024…