Wealth Management in the Age of Machine Learning

Management Wealth in the Age of Machine Learning

By Maksym Bieliai, BA Team Leader, Fintech Market Analyst at MobiDev

Wealth management services go beyond simple investment advice. It involves providing guidance to clients in making decisions about their assets as they move forward in a volatile economy. This entails weighing factors such as their personal needs, objectives, and risk threshold against one another with the ultimate goal of growing their wealth and attaining financial security.

One solution that can help asset management companies keep pace with market change is machine learning. Let’s find out how the adoption of ML algorithms impacts the development of the wealth management industry.

The Key Wealth Management Challenges

Before we explore the advantages of machine learning, let’s take a look at some of the major problems surrounding wealth management in the current market.

1. A Tech-Savvy Client Pool

With the transition of wealth to the younger generations, wealth management trends must also undergo a shift. The past decade has seen a major rise in digital solutions resulting in a digitally native client base with technologically innovative demands. It can be tricky for wealth managers to cater to the expectations of older generations while still appealing to this new breed of clients.

2. The Rise of Digital Assets

Digital assets have continued to dominate the investment industry in recent years. People all over the world are showing great interest in cryptocurrencies but also metaverse investments, ETFs, non-fungible tokens (NFTs), and others. The growing demand for digital assets calls for wealth managers to adopt new structures or a new managerial approach. Wealth management firms need an in-depth understanding of how these assets operate and how they can integrate them into their services.

3. Increased Competition

As new trends emerge in the wealth management industry, both existing players and newcomers are working towards client growth. This also presents the opportunity to reform the framework of the company to erase flaws and inefficiencies. As such, the industry is remarkably fast-paced with rookies and veterans alike rolling out new strategies for expansion.

4. Data Collection and Analysis

With the introduction of fintech apps and cloud-based infrastructure, online data collection is an option for asset management firms to add vast stores of information to aid in the growth of client portfolios. However, defined methods of analysis are essential to effectively leverage the data in question. Given the significant volume though, this will be challenging.

5. Inflexibility

Clients are on the hunt for increasingly flexible services. Wealth managers should be able to swiftly and effectively act in response to market competitors, threats to operation, and new regulatory developments. However, working beneath a strict framework and with issues from prior structures, as well as factors such as under-investment, and reliance on people can hamper flexibility.

How Machine Learning Innovates the Wealth Management Industry

As stated earlier, machine learning solutions are capable of completely revolutionizing the wealth management sector. Here are the main advantages of adopting machine learning in this industry:

1. Hyper-personalization

Machine learning makes it possible for wealth managers to provide financial or investment strategies tailored for specific portfolios even with automation. With the rise in AI app development wealth managers can manage and leverage data far more efficiently to understand and cater to the needs of clients. They can then provide clients with a unique, personalized experience as they aid them in expanding their asset holdings.

For example, California-based automated investment service Wealthfront provides digital financial planning and investment management services tailored to the needs of each client using AI algorithms. The platform analyzes the client’s savings and expenses and helps determine the optimal plan to achieve their financial goals. This model helped Wealthfront grow even during the 2020 pandemic, increasing the number of account sign-ups by 68%.

2. Sentiment Analysis

Nowadays, clients are actively monitoring their investments and it falls to asset managers to provide real-time updates about various relevant sectors. Fortunately, existing in the digital age means greater and easier access to market data and research. The use of Natural Language Processing allows wealth management companies to analyze not only numbers, but public opinion on various topics and trends in real-time, contributing to more informed investment decisions. This may include analysis of news articles, tweets of opinion leaders, etc.

NLP helps translate text-based information into quantitative data that can be used by the system for more accurate forecasting and recommendations. For instance, the US-based analytics platform StockSnips created AI Sentiment Portfolio Models to help asset managers and investment firms to generate higher returns.

3. Next Best Action Approach

Machine learning vastly increases the effectiveness of this approach which involves swiftly determining unique customer preferences. In the past, this was mostly confined to generic pointers and mass recommendations. However, in tandem with machine learning forecasting and similar solutions, wealth managers can use this approach to provide personalized advice at just the right time. Through a series of identifiers, it becomes much easier to help clients take the right path to achieve their financial goals.

For example, ML algorithms can be used to analyze stock data of different periods, as well as monitor real-time situations to predict price changes and provide this data to users to help them make better decisions. EU-based investment management startup Walnut Algorithms do this by using AI/ML technology to forecast the financial markets and predict probable outcomes. So, as you can see, ML-powered forecasting can become a winning feature for any wealth management or cryptocurrency exchange software.


4. Efficient Data Analysis

With machine learning, asset managers can take an automated and systematic approach to leverage client data. AI makes it possible to simplify the web data scraping process, and easily and quickly determine which information is relevant, allowing wealth management firms to identify their client’s needs. This results in more relevant offers and increased sales.

Examples of the application of machine learning for such purposes can already be found on the market. For example, the Morgan Stanley Wealth Management Unit has developed a system that helps financial advisors match investment opportunities with client profiles and provide more personalized offers. This policy helps the company improve customer experience and increase efficiency.

5. Enhanced Security

Machine learning provides reliable alternatives to traditional security measures. Wealth management requires a significant level of data privacy and with options such as AI biometrics, firms can ensure customer data remains safe.

Also, ML algorithms can detect fraud attempts and make decisions based on predefined rules to increase the protection of financial systems. All this happens in real-time, which significantly reduces the security risks of wealth management companies.

The Future of Machine Learning in Wealth Management

The impact of machine learning is undeniable, as in the past, various sectors have been able to improve customer interaction, buyer satisfaction, security, data analysis, and other processes. Naturally, wealth management firms are turning to machine learning for increased productivity, accurate insights, improved client relationships, and more.

As the use of AI in the wealth management industry will grow exponentially in the next couple of years, fintech companies shouldn’t ignore it. The age of machine learning is here to help wealth companies to explore innovative solutions, widen their customer base, streamline operations and distinguish their services within the industry.

The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.