Artificial Intelligence (AI) has changed the game and is no longer just a trendy term. Increased market volatility, unpredictable economic swings, and changing tax laws have high-net-worth individuals (HNIs) and ultra-high-net-worth individuals (UHNIs) searching for more intelligent ways to safeguard and increase their wealth. Thanks to AI, wealth management is now data-driven, real-time, and highly customised.
A Massive Challenge Unfolding
There has been a demographic shift in the domain of wealth management. The new generation of investors is looking more at alternative and private investments to exert more control over their capital. Wealth managers are facing significant challenges, as gone are the days when cursory performance only mattered and when similar investment strategies could apply to a broad class of investors.
Senior journalist Gary Drenik said, “Today’s investors want choices; they want customisation and alternative investment strategies. Investors and wealth management clients have different needs and risk tolerances, where equity returns and income generation need to be balanced. The wealth management industry is growing at a fast pace, and catering to individual requirements is adding pressure. Wealth advisors are expected to keep pace with market shifts, geopolitical influences, and economic factors, and wealth managers run the risk of losing clients or not meeting their clients’ wealth expectations. Ultimately, they can fall into oblivion.”
While technological advancements are opening new doors, regulatory requirements are burdening wealth management teams as well. These dynamics are creating both challenges and opportunities for wealth managers striving to stay competitive and deliver more value to their clients.
However, AI has also brought better risk-adjusted returns to wealth managers, resulting in quicker decision-making and more in-depth insights, thanks to the technology’s application to investment strategies.
Client expectations are evolving. Millennials and Gen-Z investors also want to take ESG (Environmental, Social, and Governance) factors into consideration and expect an engaging experience as they monitor their investments, while Gen-X and older generations are seeking better income generation and preservation of wealth. Traditional approaches to wealth are not satisfying the curated demands of clients.
“According to a recent Prosper Insights & Analytics survey, more than 63% of respondents in the Gen-Z category expressed that ESG issues are important to very important, versus only 43% of their Gen-X counterparts. This shift in demographics is creating new requirements and new regulations that significantly increase the burden on wealth managers and investment advisors. These changes are driving a technological disruption. Autonomous AI agents are opening new doors and creating an edge for those who harness it, but they are also a looming threat for those who do not embrace transformational change,” Drenik noted.
In another survey from the same agency, a staggering 44% of the “Boomers” generation do not trust AI to have their best interests in mind, compared to only 21% of Gen-Z’s age group. This means that the tolerance for AI is much higher with the younger generation and will accelerate the adoption of more automated services for investors.
“Furthermore, geopolitical tensions, inflation, and climate change contribute to market volatility, thereby increasing the need for real-time investment insights. Purpose-built AI is a critical component in the ability to guide investors to make timely and informed decisions. The purpose-built AI can also allow wealth managers to keep pace with regulatory compliance requirements in an era where regulatory complexity is increasing rapidly in global markets. Mobility and client demand for new international investments can add to the multi-jurisdictional requirements as well as confusion on how to properly handle governance and reporting,” Drenik said.
Let’s examine how artificial intelligence is changing the guidelines for portfolio optimisation, restructuring, and rebalancing.
PE-Based Rebalancing: Allocation With A Valuation Lens
Artificial intelligence (AI) programmes now track Price-to-Earnings (PE) ratios in real time across all indices and industries. Through comparison with past trends, this makes dynamic portfolio rebalancing easier.
Artificial intelligence systems advise lowering equity exposure to avoid overvaluation risks when PE ratios soar significantly above historical averages. Based on gaps found, the same system suggests opportunities for purchases during corrections or times of undervaluation. As opposed to responding to feelings, this keeps portfolios grounded in reality.
Equity Allocation-Based Rebalancing: A Brighter Balance
With AI, asset allocation with a 60:40 debt-to-equity ratio is simple to balance. Based on market movement, investor goals, and risk profiles, artificial intelligence continuously tracks equity weighting and compares it with the intended allocations. In difficult times, this guards against overexposure and maintains investment targets in line.
High Beta And High-Risk Portfolio Detection
Alpha, a figure that indicates a portfolio’s movement about the market, is tracked by AI-powered algorithms. In comparison to the market average, a portfolio with a high beta is likely to undergo more significant fluctuations, both upward and downward. A high-beta portfolio linked to a client with a low risk tolerance is automatically flagged for review by AI.
Financial planners can take proactive measures to align the investment strategy with the client’s comfort level and long-term objectives by rebalancing the portfolio, modifying asset allocations, or having a timely conversation with the client, thanks to this early detection. In accordance with the investor’s personal risk tolerance, this enables proactive restructuring.