The sphere of wealth management, which is all about, in the most absolute terms, making your customer richer, is going through some dramatic changes. Facing a world where the use of high-tech financial services is on the rise and new and unexpected competition from Robo-advisors, AI-based services that are just as eager to help people get richer – but instead of humans, they would put an AI behind the wheel. One day, it might even come to the point where Siri will be running everyone’s accounts from its shiny throne on iPhone 20 as human wealth management experts desperately seek new jobs… However, before that day comes (and if it comes at all), we believe, that AI-based tools for asset managers will be the next big game changer for the industry.

But let us not get ahead of ourselves.

Wealth Management As We Know It

Wealth management is a sphere of financial services that is primarily aimed at high-worth individuals. It incorporates a broad variety of services, including, but not limited to, investment management, financial planning, retirement planning, and even tax services. The key here is to figure out what your customer wants and utilize any services necessary to deliver a comprehensive strategy that would achieve this goal. It is an all-encompassing approach that views the customer’s financial life from a holistic perspective instead of only taking a few aspects of it. Such services are often offered by banks, which dominate the list of the world’s 15 largest wealth management operations.

But while wealth management is not limited to investment advice, managing a customer’s investments often plays a major role in a wealth management service. The reason for that is quite simple: investing is pretty much the most reasonable thing to do with your money when you have a lot of it because it allows you to make even more money to invest into something else or just pass on to your children. Thus, being able to come up with a solid investment strategy is a skill that no wealth manager would frown at.

In developing the strategy for each customer, you need to take into account such variables as their risk tolerance, their goals, and the time horizons they are looking at. For example, a couple in their 30s building up a college fund for their newborn child will require and an aging high net worth industrialist looking for a golden retirement would take very different approaches and portfolios.

It also makes sense to note that the optimal asset types for investment, or, rather, the proportions for allocating the funds across different asset universes also differ depending on the needs and wants of the clients. Stocks are traditionally seen as the more lucrative, but risky endeavor, venture investment in start-ups can potentially make you even richer, but is way riskier, while Treasury notes offer less income, but more security… And that is not to mention ETFs, cryptocurrencies, and other more and less exotic options on the table.

Needless to say, all of the above comes with different risk-to-reward ratios, and those need to be paid attention to when developing the investment strategy, which, in its turn, needs to be diverse enough to hedge against a possible miscalculation. But once you have identified the ratio, the next question to ask is how to pick out the best assets within their respective universes – and that is also very much a matter of strategy.

Decision-Making In Wealth Management: Evaluation & Strategizing

Once you have a more or less clear picture of how exactly you want to split your client’s funds among different types of assets, you need to figure out what exactly the money would be invested in. And while the full toolset of a vetted wealth manager is beyond the scope of this article, here, we will go over some of the most popular ones.

One of the best-known techniques for evaluating potential investments is the fundamentals analysis, which begins with the analyst going through the company’s financial statements (if we are talking about stocks, that is) and other sources to mine enough quantitative data and, to a somewhat lesser extent, qualitative data. The next step is to use all that data to make a solid estimate of what the company is worth right now. From there, we can assess how fairly its actual value is reflected in the price of its stocks. This, accordingly, will help us determine whether the stock would make a good investment or not. In doing so, of course, we would ideally also rely on the available macroeconomic data and insights into the market the company operates in. The same logic can be applied to other financial instruments, as the core idea – trying to estimate the actual value of an asset and find the under-appreciated ones – remains the same.

Fundamentals analysis lies at the foundation of the so-called value investment strategy, an approach heralded by none other than the legendary Warren Buffet. Here, the idea is to calculate the real value of the assets based on the fundamentals, identify the ones that are underappreciated by the market and acquire those in hope of benefiting from their rise. It is cheaper to buy things on sale, at a discount – that is the idea here, and for Mr. Buffet, it has definitely worked.

Technical analysis, on the other hand, largely ditches the attempts to establish the intrinsic value of an asset in favor of trying to model and predict the future behavior of its price. Here, we are talking about statistical research of trends and patterns, conducted mostly through working with historical data. The idea is to get an understanding of the asset’s behavior and try to predict it, relying on prior experience. Thus, the main object of the study is price action – or, in other words, price movements over time. It is, once again, easily applicable to pretty much any asset that you have historical data on.

Discounted cash flow (DCF) is another valuation method that is used to see whether an investment can be expected to be profitable. Here, what the analyst is estimating is, in absolute terms, again, the company’s current value, but the approach is different from fundamentals analysis in one key aspect. With DCF, value is defined in terms of future cash flows the company can reasonably be expected to generate. Simply put, you are comparing the cost of investing today with the returns the company may bring in in the future and use this assessment as the basis for your investment decision.

AI-Based Tools For Wealth Managers: New Strategies For New Era

While there is a certain virtue in sticking to your guns, technological progress, especially the rise of the AI industry, has disrupted the dogmas of the wealth management industry. As Robo-advisors join the industry to compete with the humans and whole conferences gather to brainstorm an AI-driven future for finance, things can – and will – change.

Again, this does not have to mean that we will say goodbye to human wealth managers. Human cognition has its upsides, bringing theory and intuition to the table, while AI can only approximate that through advanced statistics. AI, on the other hand, is not susceptible to emotion or stress and does not mind working pretty much 24/7 to ensure real-time market monitoring and analysis. And, most interestingly, even wealth management professionals themselves are saying that they are increasingly utilizing AI in their work.

All this makes us believe that the endgame is not in machines dethroning humans or humans rejecting machines. It is somewhere in the middle, with humans and machines sharing this proverbial throne. Artificial intelligence-enabled solutions will become a new and efficient tool in the arsenal of human professionals. AI-driven decision enhancement tools can help wealth managers by suggesting investment strategies based on customer profiles or even by helping identify the most lucrative investment solution.

As far as the latter is concerned, one of the leaders in the sphere is an Israeli daily market forecast company called I Know First. Founded by vetted financial professional Yaron Golgher and Dr. Lipa Roitman, a seasoned machine learning expert, the company has trained an AI that picks out trading signals from the most recent market data and models the price dynamics for over 13 500 assets, including stocks, currencies, commodities, and ETFs. It also keeps track of its own successes and failures, keeping a predictability indicator, defined in terms of the Pearson correlation between past forecasts and actual price movements, in its predictions. The company makes sure that only the most predictable assets make their way onto forecasts to minimize the clients’ risks.

The algorithm also makes use of genetic coding to update its own models as new market data comes in. This allows the AI to keep up the pace with the market and adapt to new conditions, making sure it can weather seasons of volatility. The algorithm also utilizes chaos theory to account for the volatile nature of the stock markets, where one small event can set the whole system off balance. The AI’s output includes forecasts for different time horizons, ranging from three days to one year, which is suitable both for long-term investors and those seeking to make a quick profit.