The rise of artificial intelligence continues to reshape the investment landscape, Inevitably, caution prevails among insurance company CIOs but AI’s growing power and sophistication cannot be ignored.
While AI presents significant opportunities for institutional investors, its adoption is still in the early stages, fraught with both excitement and uncertainty. The challenge lies not in whether to adopt AI but in determining how and when to do so effectively. This discussion is not new but it is now moving beyond exploration to implementation, writes Contibuting Editor David Worsfold.
Strategic asset allocation (SAA) is quickly emerging as one of the prime candidates for wider deployment of AI. AI’s potential to enhance SAA stems from its ability to process vast amounts of data quickly, improving decision-making and optimising portfolio construction.
AI-driven predictive models are already supporting institutional investors as advanced algorithms can simulate countless scenarios, greatly enhancing the power of existing modelling techniques. Some recent research by the Greenwich Coalition suggests, 60% of institutional investors expect AI to transform how they conduct research and shape SAA decisions. We are a long way from allowing AI to make decisions but we are very close to it becoming an essential tool for humans decision-making in investment teams.
For most CIOs the challenge is to use AI as an enabler of human decision-making, not a substitute for it. They establish feedback loops where human expertise and AI insights refine each other. By doing so, these firms create an organisational intelligence greater than the sum of its parts, a mutual benefical relationship.
Trust in the reliability of AI outputs has held back is deployment. The cautious adoption by some CIOs starts with only using data they already hold and have validated, built into their existing models. Only once they have scrutinised the AI outputs when asked to proposed fresh allocation strategies are they slowly introducing external data sources. This is where the power of AI will really start to make an impact, especially in a world where geo-political and macro-economic influences are so volatile.
For now, AI’s primary contribution to SAA is its ability to increase analytical capacity, helping investment teams assess portfolios more frequently and efficiently. Given today’s volatile markets, AI’s ability to process structured and unstructured data at scale could significantly improve the quality and frequency of SAA reviews.
Looking ahead, AI's role in institutional investing will expand beyond structured data analysis to include natural language processing (NLP) – this would be a major step change. This evolution will allow AI to process company reports, regulatory filings, central bank statements, and even social media trends, enabling more dynamic investment strategies, including thematic investing, such as for ESG-focused portfolios.
There are other issues that need to be kept firmly in focus, not least the attitude of regulators who will expect CIOs to be able to explain its use and validation – where the human sits in the loop will be crucial in this.
There are also legitimate concerns about systemic risks from widespread AI adoption. If many firms rely on similar AI models and data sources, there is a risk of herding, where models make the same decisions simultaneously, potentially amplifying market volatility. Again, the role of human expertise and judgement will be crucial in avoiding or mitigating such hazards.
The long-term vision for AI in SAA could involve dynamic portfolio rebalancing, adjusting allocations in real-time based on market conditions, though this will always be within carefully controlled parameters. The ultimate challenge for institutional investors will be defining the balance between human oversight and AI-driven automation in this evolving landscape.
AI in strategic asset allocation is still in its early stages, but the consensus is clear: institutional investors must start exploring AI’s potential now to stay ahead.