Key challenges include the fiduciary element of asset management, outdated information in some widely available models, and a preference for traditional machine learning approaches.

On the plus side, generative AI offers significant productivity benefits by helping to process massive amounts of data, make faster decisions, or generalize bottom-up analytics based on historical reports and data.

Kev Toohey, director of Atchison Consultants, noted that while large language models (LLMs) have recently gained attention, his firm has been using its own machine learning models as part of its investment process for more than five years.

"We focused on developing machine learning models that identify and isolate patterns between economic and market factors and outcomes of different investment strategies," he told InvestorDaily.

"These models help assess market conditions and develop signals for our tactical asset allocation views."

In addition, Atchison Consultants uses separate machine learning models to evaluate the characteristics of individual assets and managers. These models aim to predict the return profile of an investment against economic and market conditions.

"We see data management and visualization as a key tool for our team to effectively identify and stress test investment opportunities and have hired a data scientist who spends a significant amount of resources internally coding our investment models and reporting," said Toohey.

While acknowledging the role of AI in multi-asset portfolio optimization, Toohey emphasized that his firm still relies on "deterministic models, not AI" for portfolio optimization.

"We're balancing the benefit of repeatability of calculations for things like portfolio optimization with remaining preferred," he said.

“For less well-defined problems, such as where markets are in the cycle or how specific assets may perform in the future, we use machine learning models as informative for our analysts.

Similarly, Sebastian Mullins, head of multi-asset and fixed income at Schroders Australia, confirmed that generative AI offers advantages in speeding up processes.

However, he emphasized that traditional machine learning remains an effective tool for modeling "more complex, multidimensional relationships in data."

Mullins cautioned that generative AI is less useful for real-time decision-making because publicly available language models often rely on outdated information, such as ChatGPT, for which data only stretches back to 2021.

"This means that generative AI is providing incorrect or outdated data or news headlines," he said.

In contrast, machine learning, he noted, is valuable for tasks such as predicting future market prices, making predictions about economic variables, and identifying structural breaks or outliers in data.

"We currently use deep machine learning modes (using neural networks) to determine short-term interest rate forecasts relative to market pricing, cluster analysis to determine notional interest rates and spot currency probability distributions, along with reinforcement learning to determine the fair value of government bonds," he said.

Mike Chen, Robeco's head of Next Gen Research, emphasized that while AI will transform many aspects of how multi-asset portfolios are managed, certain areas, such as face-to-face customer service, will continue to rely on human skills.

Chen also pointed out that data availability could be a limiting factor for some asset classes.

“We believe that artificial intelligence plays a big role in all assets where there is sufficient data available. In certain asset classes where data availability is not great, the role of AI will be relatively less as AI algorithms require data to be effective,” he said.

Similarly, Toohey pointed out that asset classes with frequent market valuations, such as listed stocks, tend to be better suited for machine learning models compared to illiquid assets that rely more on subjective or opinion-based valuations.

According to Tom Boyle, CEO of derivatives-based investment manager Atlantic House Group, the biggest benefits of using AI are related to productivity.

“We don't give AI money to manage, but it allows [us] to take all the different data points where we used to be able to take 10, now we can take 30. And it allows you a broader scope or it allows you to explore different geographies at the same level of detail, but you don't necessarily have experts in every single area," he said.

Boyle expressed discomfort with relying on AI for asset classes in general at the moment, but said he sees potential for AI to improve transparency in less liquid and less transparent markets by processing data more efficiently than humans.

"I think over the next 18 months it could become a money mover, but from our perspective it's certainly a data tool to improve performance rather than actually applying money to different markets," he said.