1. There is much talk of the application of Artificial Intelligence (AI) in wealth management, but is this more hype than reality? Where is the industry, in general, in its use of AI tools and technologies?
It’s less a conflict between hype and reality and more a gradually, but strongly, growing realisation that AI has a real role to play in wealth management. If you look at the traditional tension in wealth management – between this idea of ‘white glove’ service (which is usually expensively time-intensive) and a more hands-off approach for the mass affluent – it becomes clear that neither approach is always wrong or always appropriate. It really depends on the client and the timing. But operational efficiency is always the key.
As institutions are beginning to appreciate that it’s not either/or but a question of offering both of these services efficiently, AI can massively help. That’s because AI can be used to better personalise client recommendations, whether they are provided to an adviser who interacts with clients directly, or whether these recommendations are delivered through an automated channel, or a hybrid.
2. If the application of AI is relevant for the business needs of wealth managers, where should the industry start and what do they need to do to make the most of what tools and technologies are available?
Across the industry right now, there’s a wide spectrum of AI tools and technologies uptake. Some firms are already mature in their approaches. Others are facing obstacles to successful implementation, for example a weak CRM or data repository.
For wealth managers, the key to success with AI is to start with the right business objectives, then partner with the right firm to deliver digital transformation that delivers on those objectives – such as high client service with maximum operational efficiency.
3. What are the leading use cases for AI in wealth management now and why?
AI has the most to offer in the areas of client communication, client planning and selecting the right products for portfolio to fulfil those plans.
Client communication and planning starts with understanding the client. A good AI engine should analyse clients’ portfolios and interests from online behaviour. Becoming more intelligent about who clients are, what they represent, what their preferences are and what their potential risk looks like – these are musts for client understanding and intelligence, and being data driven is critical.
But in order to make relevant, personalised recommendations, an AI engine needs to first conduct some crucial analyses on markets and products as well. This means first analysing voluminous research that would take too long for humans to do, applying natural language processing (NLP) to isolate meaningful conclusions and likewise surfacing relevant snippets of text to support the conclusions.
Product selection, in an ever more complex environment that includes the layers of choice created by ESG, means understanding potentially hundreds or thousands of products. And portfolios are growing more personalised and the interactions between the multiple products they contain need to be understood deeply as well.
Equally, planning is not just about financial products and portfolios – equally it’s about relevant wealth structuring, trusts and complex cashflow management. These are among the more complex areas of wealth management.
4. Considering those use cases identified, what are the benefits each can bring to the wealth management sector?
When deep client understanding and market, product and portfolio analysis combine with the efficiency of AI, together they provide relevant next best actions for investors and advisers alike. These can be in many domains related to client communication, planning as well as shopping for and trading financial products.
As far as client understanding is concerned, AI brings the capability to distil a lot of recommendations, suggestions and alerts, and direct the attention of the adviser to the most appropriate initiatives and actions. It will also help to identify ongoing risk and opportunity around clients and prospects.
With market analysis, AI-based capabilities can identify, select, shape and score vast amounts of information for relevance, both in general and for specific clients. AI goes beyond conceivable human bandwidth to help determine which products are suitable for Client X or are aligned with Client Y, given their unique preferences and impact aspirations. This capability is critical when there are dozens or hundreds of clients to support per adviser, even in the ‘white glove’ private banking sector, and hundreds or thousands when we move into mass affluent.
Interactions between multiple products within personalised and hyper-personalised portfolios could create new risks and impacts, as well as opportunities. There will be a need to compute all these. A clear benefit of AI is its ability to do just that, at scale and pace.
All of these sets of insights must be human-reviewable in order to allow better training of the AI engines and enable regulated wealth managers to take responsibility for the advice and decision-making. The combination of these insights with proper oversight will lead to much more automation as well as personalisation in wealth management – which is a boon for both wealth managers and clients alike.
5. Looking forward, what further use cases might come into play as the tools and technologies develop and the wealth management industry becomes a better educated user?
In the months ahead, some fundamental business issues will continue to drive serious consideration of AI in the wealth industry. These include ability to generate profit in execution only and de-coupling client service levels achieved from time spent achieving them. Then there’s a need to build in real time compliance while building proposals. The industry will also have to continue building a capacity to meet clients’ highly nuanced ESG preferences and complex time horizon requirements. AI has a role to play in meeting the challenges set by all these issues.
In addition, AI will play a key role in financial strategy construction and implementation by enabling more scenarios and simulations, right down to micro-planning. It will equip advisers with enhanced abilities to build and action a strategy that coordinates financial choices and aligns directly with the life planning goals that individual clients seek (including non-investment).
All these separate strands can and should be brought together for truly efficient and top-quality end-to-end client service, regardless of client segment. This should run the gamut from strategy to specific goal support to optimised products for each of those goals. In effect, it’s about connecting what have previously been individual dots, in a way that is fully client-centric and totally in the individual client’s best interests.
To summarise, there is enormous potential here. True AI can increase quality and time dramatically, and with great design can create amazing client and adviser experiences to address client retention, client growth and operational efficiency goals. But in order to leverage AI optimally and satisfy what clients really want from their advisers, so that they clearly recognise the benefits they gain from a trusted advisory partner, wealth managers need to partner with firms that have true AI, are masters of design and can deliver solutions efficiently. Partnering well is the key.
