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South African asset managers have been steadily incorporating artificial intelligence (AI) into their management of clients’ money and enhancement of their employees’ productivity.
The most common use of AI in the financial services sector is by insurers to forecast weather events, determine coverage, and mitigate risk.
AI is also used by banks to detect fraud, deal with low-complexity client queries, and provide preliminary credit screening.
Senior quantitative strategist at Stanlib, Dr Michael Streatfield, recently outlined how asset managers are using AI.
After a year of hands-on experience with STANLIB Multi-Asset’s bespoke AI tool MAISY (Multi-Asset AI System), the asset manager has gained unique insights into the pitfalls and potential of the technology.
The early public failures of Generative AI are inevitable when understanding the hype cycle of new technologies, Streatfield said.
Yet, for those brave enough to start, practical applications are beginning to meet corporate expectations.
Within STANLIB Multi-Asset, one such development is the private research chatbot MAISY. Unlike off-the-shelf models, this AI system is fed daily with curated data, including our meeting transcripts designed to enhance research productivity.
The benefits of integrating AI into investment research include the ability to summarise large amounts of data, generate ideas, and provide counterarguments to test insights.
In the graph below, Gartner’s Hype Cycle illustrates how perceptions of new technologies change.
Where initially, too much is expected from a new technology as it is hyped up. There is an inevitable shake-out before we settle into a higher level of productivity. Generative AI is going through such “growing pains”, Streatfield said.
Nevertheless, apart from initial missteps there are clear signs of traction by corporates. Morgan Stanley commissioned a survey on AI adoption and found around 50% of projects met corporate expectations and another 40% exceeded expectations.
Research from Boston Consulting Group tested the impact of AI access in their work across 18 realistic work tasks over 7% of their workforce.
For tasks where AI was expected to benefit employees, substantial improvements were made in quality.
The big surprise was AI usage’s ability to be a great skill leveller. The less skilled had a 43% improvement in catching up to top-skilled colleagues, and their work surpassed the non-augmented.
Stanlib thus decided to take the plunge just over a year ago and created its MAISY AI to help its multi-asset teams.
Stanlib’s MAISY chatbot is different from well-known models such as ChatGPT and Google’s Gemini in a few ways.
Firstly, it is curated based on the asset manager’s own research, creating a unique pool of data from which to draw.
It gathers not only independent investment research but also our own internal insights and transcripts of investment meetings at the team and wider business level.
These transcripts are valuable as they capture investment intuition and experience plus any “off the wall” musings. This gives richer answers than a general off-the-shelf LLM could provide.
Secondly, such a model has a substantial deal of embedded knowledge, so you do not have to explain investment jargon.
For example, it knows that the Fed means the US Federal Reserve and what the ECB is.
It also allows you substantial flexibility in output whether you want to tabulate, contrast approaches, summarise, list or do an analysis of data.
The tool can also play a helpful devil’s advocate in the team, presenting counterarguments to a held view or to list disadvantages.
Stanlib wanted this as it aligns with our team’s values of seeking out alternative perspectives and approaches. This ability to take the opposite side of a view is an input that leads to more robust investment decisions that benefit our clients and not just our culture.
Despite this, Streatfield said AI has not revolutionised asset management overnight, and MAISY is not making any investment decisions for Stanlib.
Rather, it is more of a helpful ‘team player’, making it easier to conduct research, challenge assumptions and enhance decision-making processes.
source:How South African asset managers use AI to invest your money – Daily Investor
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