How AI Affects Wealth Management – Two Perspectives

Editorial Staff 16 February 2024

How AI Affects Wealth Management – Two Perspectives

We talk to two firms about how they see AI affecting wealth management in the coming years. This is part of a series of articles we intend to produce on a topic that has gained more traction, and rapidly, in recent times.

As was evident in 2023, when the term “AI” became ubiquitous in the financial services space, the ways in which artificial intelligence could – or may not – change wealth management are closely followed topics. It is now impossible to talk about technology without reflecting on AI. (See examples of coverage here and here.)

We have reached out to firms to ask their views on what they think AI means for wealth management, and intend to return regularly to this topic. Please jump into the conversation! Email if you wish to comment.

What are the wealth management use cases for AI, what are the benefits, and how can they be measured and monitored?
Carl Woodward, joint founder and director of Simplify Consulting

"We are starting to see AI being used in a number of areas, not least in the contact centre in channels such as chatbots, but also in helping agents navigate quickly to the answers to enable them to respond to enquiries as efficiently as possible. We are also seeing it more actively adopted in the back office to help processes such as error checking and case reviews. AI is able to absorb a significant amount of data, both in real time and using historical insight to analyse information provided to determine whether it is accurate and appropriate. We will only see this continue further as it learns more and more.”

Nigel Gregory, global head of GSB Wealth
“Artificial Intelligence refers to the ability of machines to carry out cognitive tasks similar to humans, such as reasoning, problem-solving, learning, and adapting to new information. In the context of wealth management, AI has the potential to provide valuable insights into clients' spending and saving patterns, as well as their financial goals. This can help advisors and customer-oriented professionals to offer highly personalised content and advice. 

This type of AI service is expected to become commoditised in the future. Access to advanced forms of AI will advance and streamline the investment and risk management process, impacting investment outcomes that should surpass any previous expectations.”

How should wealth managers explain how to show the value-add impact of AI, and be clear about how they use it?
Woodward: "Transparency is really important, to build trust with customers and to ensure that they understand the extent to which it is being used. Already we are seeing some nervousness when it is unclear whether chat sessions are being conducted with a human or a computer, which only goes to erode trust – something that is so important in financial services. We think wealth managers should be really clear about where AI is being used and how it is being used; in the majority of cases it is about being able to provide service efficiently and accurately which is ultimately designed to benefit the end customer.

Rather than being afraid of the impacts and remit of AI, providers [should] impress upon customers the value it can play in delivering a more tailored, personalised experience.”

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."

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