This article explores the various ways that AI technology is changing the face of wealth management in the UK and much of the world.

One important theme in global wealth management is how banks and other institutions are developing use of Artificial Intelligence (AI), automation and machine learning. The volume of data is a big issue, as is the need to be on the alert for regulatory red flags, market disturbances and changed client requirements. Debate continues on whether the rise of AI threatens to put people out of a job or instead makes their work more effective, even more pleasant. We continue to track this trend and invite readers to comment with their thoughts. 

This news service republishes the following white paper, with permission from Level E Research Limited. The author is Dr Sonia Schulenburg, CEO of Edinburgh-based Level E Research.

Dr Schulenburg holds a PhD in Artificial Intelligence from the University of Edinburgh and a BEng in Computer Engineering (summa cum laude; 1st Class Honours) from the ITAM in Mexico City, a Professional Certificate in accounting from the University of California, San Diego and a postgraduate degree in Corporate Strategy and Finance from Edinburgh Napier University, where she graduated with distinction in both.

This news service has undertaken its own research work into how Artificial Intelligence affects wealth management, such as here in 2017. Clearly, the pandemic has accelerated use of this technology in certain respects although it is not the only driver.

The usual disclaimers apply to content sent from external providers; to respond, email tom.burroughes@wealthbriefing.com and jackie.bennion@clearviewpublishing.com

Traditional wealth management
For nearly a century the core objective of wealth management has been to help clients plan for their financial future to attain peace of mind, while keeping up with dynamically changing markets. The primary role of a wealth management financial advisor is to use a diligent consulting process to know and understand the client’s needs, expectations and risk tolerance given their current situation, and then construct a personalised investment strategy by using a broad range of financial products and services. 

Once the original investment plan is drafted, reviewed and executed, the manager meets with the client in order to present results, update goals and ultimately, rebalance the financial portfolio. Moreover, the presence of a continuous integration process evaluates new services and the manager promotes them in order to offer a lifetime solution.

Traditionally, small or large scale wealth management firms make use of financial consultants or advisors to get in touch with clients, construct portfolios (asset allocation). Then, based on mutual agreement, they will proceed to place orders in previously identified markets via third-party brokers. Accounting for this life cycle entails assets under management fees, commissions on the investment products they sell, broker and operating fees, variable premiums on net returns, etc. In fact, a survey [1] found that the median advisory fee of assets under management is 1 per cent for up to $1 million, but the all-in cost of a highly efficient advisor averages at 1.65 per cent. 

Specifically, for the asset allocation process, many advisors will offer securities that are ‘hot’, in great demand or passive ETFs which are familiar to them. The sole consideration of assets because they are popular in the news or recommended by peers or brokers is not enough in the changing and revolutionised market. The only true advantage we can rely on is to analyse and trust the data. 

In the following sections we will present current challenges in wealth management and analyse some examples of AI used in the financial industry.

Upcoming challenges
As suggested in [2, 3, 4], the foreseeable future imposes a new set of challenges for the wealth management industry. One of the main concerns is the incremental addition of a new generation with fresh and different investment ideals while keeping the trust of their existing HNW investors. The target audience is expanding, and there should be a place to accommodate everyone in this new tech-based economy.
We strongly believe that the tech-savvy younger generations demand comprehensive and goal-based personalised wealth offerings and wealth management must evolve and use emerging AI-based approaches such as those in healthcare diagnostics, precision medicine/personalised medicine.

Therefore, the times of change have arrived and we should address the following upcoming needs:

1. The combination of human, virtual and automated advice represents an area of opportunity not effectively addressed by current firms [2]. The adoption of new generational sectors, especially under the age of 60 (including Gen X, Millennials and Gen Z) faces truly different needs than Baby Boomers. For example, most of the new generations (85 per cent, 91 per cent and 97 per cent respectively) require banking as well as insurance products (compared with 47 per cent of Baby Boomers); 

2. The clearest shifting of generational interest is the adoption of lifestyle preferences and concerns about the environment. For example, the adoption of ESG based portfolios [4]; 

3. There is a global tendency to avoid generic advice. HNWI’s want more personalised advice; 

4. Cultural differences embracing technology and trust rather than traditional insider advice imply exhaustive quantitative analysis at the time of portfolio selection; 

5. The transfer of wealth to new generations will inevitably move capital from traditional obscure funds, to more on-demand internet platforms with instant access (“wealth is about to change hands”); 

6. Old school financial advisors are ageing and, while they will not disappear, a big demographic change in the finance industry is on the way. In fact, advisors are ageing and leaving the industry faster than firms are replacing them [5]. Therefore, the new generation of advisors will also demand innovative technological solutions; and  

7. The pressure on maintaining competitive returns given increasing trading fees and regulatory requirements [1].