How impactful could Generative AI be in financial markets?
Gen-AI is far more than just a trendy term; if you stop to consider it, it’s akin to a significant technological revolution, comparable in magnitude to the advent of the steam engine, electricity, computers, or the internet.
Generative AI enables the provision of on-demand intelligence, which is especially crucial in finance. We believe that early adopters will gain a significant competitive edge, pioneering the application of this technology.
How is adoption across the industry coming along?
From a technological standpoint, what’s exciting is that the entry barriers are quite low, while the potential outcomes can be remarkable. Models are constantly improving and becoming more cost-effective. This technology can be leveraged to enhance current workflows, or it can even inspire the creation of innovative working methods that result in exponential productivity growth.
The capability to customise the model with your specific data and train it to perform in a tailored manner will be critical.
What’s the best approach when it comes to implementing this technology?
I believe the initial step should be to focus on governance: establish a concise AI manifesto that clarifies acceptable applications of the technology. For example, guidelines could include maintaining a human-in-the-loop approach (humans should be the ultimate arbiter of any AI-based decisions); restricting Generative AI use to internal purposes only; and avoiding the incorporation of Generative AI into client interactions, trading activities, or any applications that might directly impact market prices.
The second step should be ensuring you have a flexible platform that is model agnostic to allow for easy transitions between LLMs like Gemini and ChatGPT. At this stage, it’s important not to become overly reliant on a single provider. Our aim is to provide access to a Generative AI to all our staff, enabling them to engage with the technology in a data-protected setting and contribute innovative application ideas.
The third step, which is a bit more complex and what we’re currently focusing on, involves feeding the model with our internal data and training/fine tuning it to address our specific use cases.
Has market sentiment towards AI changed?
It’s clear that there is a palpable sense of apprehension among many, and their concerns are valid. It is crucial to acknowledge those fears because of the inherent risks associated with this technology, which underscores the need for clear guidelines to manage it effectively.
In the future, there’ll be a distinct division between entities that superficially engage in ‘AI washing’—perhaps simply automating their meeting minutes or other basic tasks—and those who harness GenAI to its full potential, employing it to analyse sentiment, ideas, and conversations. It’s in these areas that GenAI will truly stand out, providing a valuable enhancement to the conventional quantitative analyses focused on pricing and financial data.