Towards optimal wealth management based on AI – Study

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

An important theme in global wealth management is how banks and other institutions are developing the use of artificial intelligence (AI), automation and machine learning. Data volume is a big issue, as is the need to be on the lookout for regulatory red flags, market disruptions and changing customer requirements. The debate continues over whether the rise of AI threatens to put people out of work or makes their jobs more efficient, even more enjoyable. We continue to follow this trend and invite readers to comment on their thoughts.

This news service republishes the following white paper, courtesy of 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 with distinction) from ITAM Mexico City, a Professional Certificate in accounting from the University of California, San Diego and a postgraduate degree in business strategy and finance from Napier University in Edinburgh, where she graduated with honors in both.

This information service has undertaken its own research on the impact of artificial intelligence on wealth management, as here in 2017. Obviously, the pandemic has accelerated the use of this technology in some ways even if it is not the only driving force.

The usual disclaimers apply to content sent by external providers; to reply, send an e-mail [email protected]and [email protected]

Traditional asset management
For nearly a century, the primary focus of wealth management has been to help clients plan their financial futures to achieve peace of mind, while staying in tune with rapidly changing markets. The primary role of a wealth management financial advisor is to use a diligent consultation process to know and understand the client’s needs, expectations and risk tolerance given their current situation, and then build a strategy for personalized investment using a wide range of financial products and services. .

Once the initial investment plan has been drafted, reviewed and executed, the manager meets with the client to present the results, update the objectives and, ultimately, rebalance the financial portfolio. In addition, 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 companies use financial consultants or advisers to get in touch with clients, build portfolios (asset allocation). Then, on the basis of a mutual agreement, they will proceed to the placing of orders on markets previously identified via third-party brokers. Accounting for this life cycle involves assets under management, commissions on the investment products they sell, brokerage and operating costs, variable premiums on net returns, etc. Indeed, an investigation [1]found that the median asset under management advisory fee is 1% up to $ 1 million, but the overall cost of a highly effective advisor averages 1.65%.

Specifically, for the asset allocation process, many advisers will offer “hot”, high demand securities or passive ETFs that are familiar to them. Merely considering assets because they are popular in the news or recommended by peers or brokers is not enough in a changing and revolutionized market. The only real benefit we can count on is to analyze and trust the data.

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

Challenges ahead
As suggested in [2, 3, 4], the foreseeable future poses a new set of challenges for the wealth management industry. One of the main concerns is the gradual addition of a new generation with new and different investment ideals while maintaining the confidence of their existing HNW investors. The target audience is growing and there should be a place to welcome everyone in this new technology-based economy.
We strongly believe that the tech-savvy younger generations demand comprehensive, goal-based personalized wealth offerings and that wealth management must evolve and use emerging AI-based approaches such as health diagnostics, precision medicine / personalized medicine.

Therefore, the times for change have arrived and we must respond to the following upcoming needs:

1. The combination of human, virtual and automated advice represents an area of ​​opportunity that is not effectively addressed by today’s businesses. [2]. The adoption of new generational sectors, especially the under-60s (including Gen X, Gen Y, and Gen Z) faces very different needs than baby boomers. For example, most new generations (85 percent, 91 percent and 97 percent respectively) need banking products as well as insurance products (compared to 47 percent of baby boomers);

2. The most obvious generational shift in interest is the adoption of lifestyle preferences and environmental concerns. For example, the adoption of ESG portfolios [4];

3. There is a worldwide trend to avoid generic advice. HNWIs want more personalized advice;

4. Cultural differences in favor of technology and trust rather than traditional insider tips require exhaustive quantitative analysis at the time of portfolio selection;

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

6. Old-fashioned financial advisers are getting older, and while they won’t go away, a big demographic shift in the financial industry is underway. In fact, advisors are aging and leaving the industry faster than companies replace them. [5]. Therefore, the new generation of advisers will also demand innovative technological solutions; and

7. Pressure to maintain competitive returns given rising trading fees and regulatory requirements [1].


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