Gregory: "AI-powered portfolios operate differently from human portfolio managers or conventional asset allocators since they rely on a distinct critical thinking process generated by varied analyses. Unlike human portfolio managers, AI systems do not depend on any pre-existing assumptions or beliefs about how markets function. Instead, they are designed to develop their knowledge from data and predict asset behaviour and expected portfolio risk.

Wealth managers have a crucial role in managing investments for their clients. They need to carefully identify which assets to include in the portfolio, define investment objectives that align with the client's goals, and impose some boundary conditions to target different risk-return profiles. Additionally, they should maintain an oversight role to monitor the portfolios and ensure that they are on track to achieve the desired outcomes.

Once these steps are taken, wealth managers can allow AI to take over and make investment decisions. This approach ensures a clear separation of roles and accountabilities. It is important that all AI investment decisions are transparent and disclosed to the clients. This will help build trust and confidence in the investment process.

Wealth managers need to refrain from regarding AI decisions as advice and selectively adhering to some while disregarding others. This approach lacks accountability and can significantly undermine the value added by AI systems. Moreover, such an approach could expose advisors to the risk of making poor investment decisions they may not be qualified or licensed to make. It is of the utmost importance to ensure that AI technology is appropriately and responsibly utilised in wealth management, to uphold professional standards and meet regulatory requirements."
 

Can AI have a meaningful impact on fees, costs, profits, revenue generation and building a new source of clients?
Woodward: "AI can be transformational in how it enables a business to re-invent its service proposition, drive down the costs to serve and provide customers with an experience that a human-led service model is simply unable to replicate at scale. We are already seeing simple contact centre interactions being replaced by AI, where it takes traffic away from contact centre queues and enables agents to focus on complex enquiries requiring a human touch.

As operations functions become more efficient and find new ways to leverage AI, the cost base of the organisation will fall and, in the spirit of value assessment, those fee structures should also come down as well. If AI is used effectively and, where benefits can be realised that help all parties, there is an immediate uptick in revenue and profit, resulting in a reduction in fees incurred by customers.”

Gregory: "Conventional discretionary portfolio management models are much less scalable than AI-powered portfolio management solutions, which can handle much larger volumes of assets while requiring only a fraction of the input costs.

The traditional method of discretionary portfolio management is not easily scalable due to the specialised nature of certain critical functions involved in managing portfolios. These functions include research, idea generation, portfolio construction, capital allocation, and risk management. Wealth managers often emphasise the importance of specialised knowledge and resource specialisation as key value proposition elements. However, this severely limits product scalability.

Sophisticated artificial intelligence engines can execute numerous investment strategies at once, thereby streamlining the cost structure of traditional portfolio management models while delivering superior investment outcomes. At minimum, AI-powered portfolios can function as an effective diversifier of conventional investment propositions.”

How can AI intersect with areas such as ESG investing, behavioural finance, risks management more generally, client reporting, and more?
Woodward: "We live in a data rich but information poor world. We collate significant volumes of data but struggle to analyse it quickly and efficiently as the detailed analysis to make data meaningful is simply too extensive for humans to interpret. Technology innovation in respect of processing power and data analytics has come on leaps and bounds in recent years and with AI now being adopted more widely, it has the potential to significantly reduce the time it takes to analyse data – and the extent of the data set used. 

Automation and artificial intelligence as it “learns,” will enable more sophisticated analytics, more accurate outputs and all identified more quickly, which will ultimately benefit a number of activities, including ESG analysis, performance reporting, client reporting, and behavioural finance, etc."