In an article written by Alvin Chia, senior vice president – head of digital asset innovation at Northern Trust, and Krishan Dave, head of investment risk and analytics services at Northern Trust, the two said that while ChatGPT and generative AI are new to the public consciousness , the world is changing rapidly to understand the implications of technology.

“It has the power to change the face of every industry on the planet. But we believe that in the short term, the role of generative AI will be limited to supporting existing research and data collection, rather than being the primary driver of decisions,” the pair said.

“Our current knowledge of generative AI does not allow us to distinguish false positives from real data, which leads to additional risk for portfolio managers. But that doesn’t stop us from taking a longer-term view of how generative AI could shape the investment management industry.

As for exactly how AI might impact investment management in the future, Mr Chia and Mr Dave said real-time dashboards powered by generative AI could help managers spot trends.

“Along with market data, generative AI can combine esoteric data sets, such as sentiment analysis or keyword searches, that generative AI deems most relevant,” they said.

“It can reveal hidden trends or ‘black swan’ events that have not been seen before, providing a unique view of the investment horizon faster than ever before.”

The pair added that the technology could also be used to improve and refine portfolio optimization, which they say often relies too heavily on historical data and focuses too heavily on comparing return maximization with the level of investment risk.

“Using learned knowledge of a manager’s style and investment philosophy, generative AI can create unique optimization strategies, helping to create individualized stock allocation proposals and overlaying a client’s investment and ethical policies to aid further review” , said Mr. Chia and Mr. Dave.

“AI has the potential to make a mark in the investment analytics space as well. Traditional ex-ante risk models or performance attribution can be consigned to the analytical scrap.

“Instead, models could follow continuous and iterative improvement paths as generative AI seeks to modify and optimize models based on investment style and market events, making models more relevant to the portfolio manager and the client.”

Other areas the pair noted could be impacted by generative AI include helping market makers predict demand for liquidity and reading market conditions, as well as supporting the investment decision-making process to allow investors to move from intuition to data management.

“The power of generative AI will not be limited to the direct investment process, but operational efficiencies can also be achieved,” they said.

Mr. Chia and Mr. Dave added, “The actual integration of generative AI has also not yet fully materialized today. An organization needs to consider where it feels comfortable introducing AI and the areas that are “out of bounds”.

“Having a clear vision describing what needs to be achieved and what success looks like can help drive development, while of course being aware of potential biases and emerging ethical issues based on the datasets.”