According to McKinsey, 100,000 financial advisors will be required over the next decade to serve American investors. The combination of aging advisors following their clients into retirement and a rising demand for advice (because decumulation of retirement assets is infinitely more complicated than saving for retirement) will require wealth management firms to alter the way they deliver advice and cause them to search for tools that can help them deliver personalized advice at scale.
AI and automation will play a critical role, just as multiple waves of financial technology have helped scale the advisory business over the past 40 years. AI is landing in a financial industry built on a generation of math-driven, quantitative investment strategies. Building on this foundation, generative AI allows us to fully capitalize on our existing financial data, blend it with client information and turn out hyper-personalized strategies and bespoke investment solutions.
AI’s trajectory in finance began with early forays into quantitative analysis, rules-based systems and basic neural networks in the 1990s. Target risk and target date funds delivered institutional asset allocation at scale, in a ready-to-buy package. Portfolio optimization tools and retail separate accounts allowed even more personalization and could incorporate existing holdings, optimize tax efficiency and incorporate preferences like ESG.
AI performs three critical functions that these innovations cannot: delivering personalized asset-liability matching at scale, anticipating market events and modeling out scenarios that may jeopardize clients’ financial plans, and communicating personalized messages at scale.
Personalization: The New Gold Standard
AI’s ability to facilitate “mass customization” is nothing short of revolutionary. A “Segment of One” is achievable and every client has become a “SPUD” – “SPecial, Unique and Different.”
AI’s ability to facilitate “mass customization” is nothing short of revolutionary.
Managers using AI can leverage client data including demographics, risk tolerance and goals, to craft unique portfolios that adapt as circumstances evolve. Managers can build strategies for “goals-based investing” tailored to the life-events of their clients. Examples of these bespoke strategies range from traditional assessment of clients’ desire for capital preservation, long-term growth or risk engagement, to the development of custom thematic portfolios such as clean energy, healthcare innovation or emerging technologies, in line with an investor’s vision.
Accumulation is relatively simple math, but retirement income distribution is a multi-variate equation with several unknowns including longevity, market returns, sequence of returns, inflation and healthcare costs. Managing portfolios to match each client’s personal spending pattern is infinitely more complicated than managing wealth accumulation.
Personalized cash flow planning allows the advisor to deliver more value at scale. Clients have multiple deferred goals (e.g., an aging roof that needs to be replaced someday or a car that can be driven a few more miles) and AI can retain those goals and allow the client to realize them when the market delivers outsized returns as it has done over the past few years. And AI can help wealth managers comb through spreadsheets to find precisely the optimal securities to sell when a client needs cash to match their expenses.
Managing Volatility With Vision
A good example of applied AI can be found in the management of market volatility. For decades, risk managers have relied on historical data to predict future risks – a limited approach – but now there are better alternatives. AI changes the game by offering richer, more dynamic, closer-to-real-time scenario analysis and the milestones to watch.
A wealth manager using AI can build trust by showing they are professional and prepared for market events. Tools that were only available to the most sophisticated investors managing institutional accounts are now available to wealth managers to help them evaluate risks in individual client portfolios.
While AI won’t foresee every black swan, it can identify stresses and trends and help managers to leverage strategies appropriate for individual clients – those that are risk averse and focused on asset protection, as well as those risk-engaging clients who proactively seek out market opportunity borne of volatility.
Personalized Communications
Personalized portfolio commentary will become the new norm.
Advisors only get credit for doing good work when they communicate the value of that work to their clients. AI can help advisors deliver personalized communications to their clients at scale. Where overall market commentary is a commodity (or, even worse, irrelevant noise), personalized portfolio commentary will become the new norm. Questions about the impact of market volatility on specific portfolios can be answered at scale and answers can be delivered automatically to clients who worry that their retirement may be at risk.
Clients who panic and call their advisor when the market drops a few percentage points could receive an automated update from their advisor showing them how their goals are still on track. Today, personalized communications need to be reviewed by compliance and delivering them in real time would be unfeasible. With a marketing agent and a compliance agent communicating and working in concert, personalized materials can be delivered at scale in real time.
Furthermore, these AI written communications are not only personalized to the client’s portfolio, but the summaries can be delivered in the voice (or avatar) of their advisor via audio or video delivery.
Leveling The Playing Field
This widespread use of AI across wealth managers of varying sizes and personalities may transform the structure of the industry. While some market analysts have predicted that AI will deepen the divide between large firms and small players, we actually see a leveling of the playing field. Large firms are actively using AI to scale operations and reduce costs. But, at the same time, smaller firms are duplicating these efficiencies in an attempt to punch above their weight.
Smaller firms are duplicating these efficiencies in an attempt to punch above their weight.
The dramatic expansion of AI tools will empower boutique wealth managers as never before and enable those wealth managers adopting AI to serve more clients and close the advice gap that McKinsey identifies.
Looking ahead, we will see tighter integration of AI into every facet of finance, from compliance to client engagement, to financial planning and portfolio construction. Wealth managers that use AI to lower costs, while at the same time improving service quality, will be able to expand the market for their services, delivering mass affluent clients the same level of sophisticated services previously reserved for ultra-high net worth individuals.
That’s not just innovation; it’s transformation.
John Sweeney is President of Praxis Solutions, a provider of custom AI and blockchain solutions for wealth and asset management.
