How AI is transforming the Wealth Management industry

We see a lot of articles on how Artificial Intelligence (AI) is transforming or disrupting various industries. A lot of these transformations apply to any industry or sector. So, the automation of back-office processes or using the latest LLM-built chatbots for improved communications with customers will help a bank, an energy company, or a toy manufacturer.

Occasionally these articles zoom in on sectors that we are particularly interested in like Wealth Management. A couple of interesting recent examples are:

• Yahoo Finance article titled “AI will bring ‘carnage’ to wealth management, strategist says”. It argues that AI will disrupt the wealth management industry by enabling low-cost and high-quality robo-advisors to replace human advisors. This will force smaller companies to consolidate and spend heavily if they want to remain competitive. To back this finding, the article refers to a recent PwC survey predicting “that 1 in 6 asset and wealth management companies will be bought or shut down in the next five years”.
• Blackrock article titled “How AI is transforming investing”. It details how large fund managers have evolved to using LLM-like Transformers to analyse text sources and generate return forecasts. It is an interesting read that provides a primer into how these quantitative portfolio allocation and management tools work and what is the state of the art for large Wealth Managers.

We don’t agree with everything that these articles say or how they are positioned. For example:

• It may be fair to say that robo-advisors can replace humans for customers who are highly digital native and content with passive investment. However, many customers still want (and will continue to want) personalised human attention and individual portfolio construction and management.
• While it is fascinating and impressive how well Transformers appear to predict future returns, customers still want (and will continue to want) to invest in more traditional products based on Value, Growth or passive strategies.

Scale

One thing that both articles have in common is the relevance of scale. Scale is necessary to spread the costs of robo-advisor development across more customers. Scale also enables you to have the brainpower and skills to build the best predictive models and algorithms.

This does not mean that smaller Wealth Managers are doomed to fail or be consolidated. But it does mean they should start thinking about how they can adapt to and benefit from AI.

This can be as simple as starting to use AI to do more straightforward tasks (e.g. LLM-generated job descriptions or AI-produced meeting transcripts) or using AI to augment/speed up your internal work (e.g. see our recent article on policy writing).

At the same time, smaller Wealth Managers should define which parts of the business they want to be in the longer term and how (e.g. they may decide to distribute the best third-party Quant products, but keep other portfolio management capabilities in-house). They should also continuously monitor what AI solutions are available in the market for their size and type of business (new solutions are constantly coming out, so this cannot be a one-off exercise). For example, a quick search yields various third-party tools like AlphaSense or Kensho that a small Wealth Manager could trial in order to better predict future returns (Note: we have not tested these tools ourselves, so this is not an endorsement).

How this plays out will in part depend on the quality of tools that get developed over the next few years and how well the smaller Wealth Managers adapt to the changing environment. Wealth management clients are often loyal and a lot of the demographic is unlikely to change in the short term, but Wealth Managers should always be looking ahead.