Towards Optimal AI-based Wealth Management - Study

Dr Sonia Schulenburg 5 October 2021

Towards Optimal AI-based Wealth Management - Study

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.

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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].

The AI impact 
As shown in the research conducted by Accenture [6], there is approximately $78 trillion of assets ready to be captured by wealth managers (due to the global expansion of the middle class and wealth created by a new generation of entrepreneurs, e.g., those who decided to embark into their own business thanks to the great amount of information available on the web and the almost zero cost of reaching customers through social media). For this reason, AI presents a good fit for targeting this market since it provides:

--  Major client engagement through the use of web-based platforms by the advisors and their own clients; 
--  It helps to elaborate better financial products such as portfolio optimisation by using machine learning; 
--  User experience is enhanced by providing a transparent 24/7 readily available source of information in a website; and 
--  AI increases productivity and operational efficiency since the big majority of the tasks are performed by AI-automated systems (e.g. portfolio allocation given the clients preferences, automated order placement and free access to all accounting services).

The adoption of AI is not reserved for fintech start-ups. There is a clear adoption by major institutions in the market proving its fundamental value to address the new market challenges. Actually, there is a trend of big corporations incorporating AI-based solutions in their investment and portfolio allocation repertoire. Examples include Abrdn, which recently acquired Exo Investing [7], a move that is intended to deliver a 24/7 digital wealth management solution via an app and JP Morgan has also bought another fintech firm: Nutmeg (containing approximately £3.5 billion in assets under management for more than 140,000 clients) [8]. Perhaps, one of the most successful stories of AI in wealth management is the case of The Next Best Action system by Morgan Stanley, which provides their financial advisors with machine learning algorithms to identify investments of interest to particular pre-existing clients [9]. From this practice it has been shown that continual engagement with the client has improved the overall experience and motivated substantial valuable changes in their winning strategy. 

Machine learning made simple
Machine learning can be seen as a subfield of AI concerned with the incremental learning of artificial systems from data with the central objective of taking advantage from previous experience.

AI/ML makes the investment process better by systematically making an abstraction of the wealth management process and transforming it to a pipeline of the following automated tasks:
-- Preference profiling. Smart front-end interfaces gain insight into the current client situation by providing an automated questionnaire which keeps track of the answer history and then mathematically transfers this information into a classification process for user profiling. For example, in terms of risk tolerance we can segment clients into cautious, balanced or aggressive. Using transfer learning, we can also significantly reduce the amount of time it takes to complete these questionnaires as one of the primary characteristics of younger generations is lower tolerance for completing forms; 
-- Asset allocation. Based on previously trained models and the client's profile, an AI-based system infers an optimal solution for the allocation of wealth by using a predefined portfolio or dynamically tailoring a new option for covering specific needs. From our previous example, cautious clients are immediately assigned a portfolio with a large majority of fixed income securities, a small proportion in equities and a minimal proportion of cash and equivalents. Balanced clients are automatically assigned portfolios with an equal amount of fixed income securities and equities. Aggressive clients take a minimal proportion of fixed income securities and a major part of equities, keeping a minimal amount of cash and equivalents; and 
-- Order management. Clients can opt for a fully-automated solution that places orders in the market autonomously, or they can impose stricter controls for order approval.

What’s next for wealth management?
My vision of the wealth management sector of the future involves the construction and development of data-driven machine learning solutions. Specifically, extending the notion of modern portfolio theory by driving the investment process through the use of automated AI-based systems for asset allocation, order management and placement, reporting and portfolio analysis. Clients of the future are -extremely- tech savvy, therefore they should be able to enter a holistic application designed to meet their needs, and at the same time being accessible from any computer, mobile device or tablet. 

Disruptive technologies should  aim to revolutionise the investment process in wealth management, providing an automated combined solution offering:
--  High returns over a low cost. The new business model should use a data-centric paradigm where machine learning algorithms are totally in charge of automated asset allocation, supplying conventional human intervention in portfolio creation (having a proven performance over passive ETFs offering uncorrelated portfolios to major indices reducing risk). Web-based fund monitoring and accounting tools make clients totally independent in any reporting or order management tasks.  
--  Full transparency. Automated solutions should provide the client with full 24/7 access to the most detailed information regarding allocation, exposure data and portfolio risk. 
--  Excellent client experience. Clients should be allowed to gain instant access to their data taking advantage of high levels of automation, efficiency and mobility on demand.  
--  Tech-driven advice (fully or partially automated). Full automation produces an optimal tailored portfolio given a personalised requirements elicitation process. Furthermore, direct communication to the client enhances the investing process by aligning those automated recommendations to special requests by the clients (e.g. interest in a sustainable ESG approach, risk-aversion level modification, or a different rate of return).

Integration is paramount. Currently, incredible efforts need to be put in place in order to integrate several service providers and their outputs to access a portfolio management system to keep track of performance and exposures; a risk management system to visualise historical risk-metrics (volatility, Sharpe ratio, etc.) by considering benchmark indices and performing factor analysis in order to statistically explain the nature of the returns; an order management system to review and control any order to be executed as well as keeping a history of previous orders for reporting purposes; an information management system for having direct access to all the relevant information about their investments and a data lab to allow them to experiment with back-testing scenarios of their strategies.

The use of AI in investment management is set to revolutionise the industry. A disruptive holistic approach described in this paper fills the gap between end clients and targeted performance from their portfolios by automating the entire investment process. Financial advisors need to augment their skills with the advent of the new trend of technologies in order to have a competitive advantage [10]. Operational costs can be highly reduced by opting for a fully-automated solution.


The future is bright. I am optimistic that for these new generations of investors a well-deserved and trustworthy set of opportunities will (and can only) be offered through innovative technology.

About the author:

Dr Sonia Schulenburg is director, and investment committee member of Level E Capital SICAV plc, a Maltese multi-fund investment company dedicated to autonomous investing. She 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.

We would like to thank Steve Dyson from Investment & Wealth Management Consultants Ltd for the interesting conversations, support and guidance while conducting this research paper.


[1] Financial Advisor Fees Comparison – All-In Costs For the Typical Financial Advisor? Website. July 31, 2017. Accessed on August 25, 2021.

[2] Investors Want More Diversified Financial Products and Customized Advice from Their Wealth Managers, Accenture Report Finds. Business Wire, A Berkshire Hathaway Company. Online Article. August 24, 2021. Visited on August 25, 2021.

[3] The Future of Wealth Management. The Street, Retirement Daily. Online Article. August 23, 2021. Visited on August 25, 2021.

[4] Thematic Research into Wealth Management – Players Include DBS, Betterment and UBS Among Others –. Research and Markets, The World’s Largest Market Research Store – Yahoo Finance. Online Article. August 17, 2021. Visited on August 25, 2021.

[5] 10 Disruptive trends in wealth management. Deloitte technical paper. Accessed on August 25, 2021.

[6] AI in wealth management: Built to scale. Accenture Capital Markets. Online Article. December 02, 2020. Visited on August 25, 2021.

[7] Abrdn acquires AI solutions business Exo Investing. Investment Week. Online article. August 10, 2021. Visited on August 25, 2021.

[8] JP Morgan buys Nutmeg. Fund Europe. Online article. Accessed on June 23, 2021.

[9] The Pursuit of AI-Driven Wealth Management. MIT Sloan Management Review. Online Article. July 07, 2021. Visited on August 25, 2021.

[10] AI won’t replace investment managers but it could improve returns. World Economic Forum. Online Article. May 24, 2021. Visited on August 25, 2021.



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