Will the end result of new artificial intelligence developments be lower fees and better risk-adjusted returns for investors?
Like everything else, the investment industry is going to see some significant changes as artificial intelligence technology improves and adapts. Most of these changes should result in lower fees, and, quite likely — but less guaranteed — better investment performance for most investors. But don’t worry if you are an adviser or an analyst. Contrary to popular opinion, robots are not going to replace financial advisers. Most clients will still pay for a human touch, or at minimum someone to blame when things go bad. On the analytical side, most “black box” stock screens will fail to capture the nuances of a money-losing company that might still be the next-best thing, or will fail to capture the excitement noted in a CEO’s voice when he or she is discussing the company’s robust prospects.
Let’s look at five potential AI developments in the investor sector. In keeping with the theme of this column, we also consulted ChatGPT and Perplexity on this topic. Both AI services had surprisingly-similar viewpoints.
Advanced data analysis and pattern recognition
AI systems can analyze vast amounts of financial and alternative data at speeds and scales far beyond human capability. These systems identify market patterns, trends, and investment opportunities that traditional analysis might miss, leading to more informed and timely investment decisions. This data-driven approach is especially valuable in sourcing deals, uncovering hidden market trends, and flagging emerging industries or undervalued assets before competitors notice. That being said, this trend is more likely to favour traders than long-term investors. Longer term investors still have time, if a company or trend really is going to be the next greatest thing. If you buy a stock that is going to compound 10,000 per cent over a decade, you do not need to be there for the first 200 per cent.
Automated portfolio management and optimization
AI-powered platforms can enable continuous monitoring and real-time adjustment of investment portfolios. These tools can automatically rebalance asset allocations as market conditions change, ensuring portfolios remain aligned with investor goals and risk tolerances. Machine learning algorithms can learn from historical data to refine their optimization processes, making smarter, more accurate recommendations over time. This thesis makes sense, as it is essentially what advisers do now anyway, in trying to perfect clients’ appropriate asset allocation mix. By making such determinations more automatic, advisers will free up more time for personal consultations with existing clients, rather than plugging in numbers and expectations all day.
Enhanced risk assessment and mitigation
AI models can provide sophisticated risk analysis by evaluating multiple risk factors simultaneously, including market volatility, correlation risks and company-specific threats detected through news and regulatory filings. Unlike traditional risk models, AI can dynamically map interconnections between sectors and assets, identifying how stress in one area could cascade through the financial system, and suggesting effective hedges. This thesis might be more helpful for hedge funds, as most individual investors do not do a lot of their own hedging. Most investors simply hold more cash if they are worried. But advisers can still look at enhanced risk management techniques to understand how different assets will interact, and it could be very helpful in monitoring an adviser’s book of business to see what total risk they are taking on. Or it could be helpful at the company level to prevent a Lehman Brothers situation where actual risk is much higher than what is perceived.
Personalized investment advice
AI enables a new level of personalization in investment advice, going beyond standard risk questionnaires. By analyzing an investor’s full financial picture, including spending habits, career trajectory, geographic location and even life events, AI can deliver highly individualized recommendations. These systems adapt over time, continuously learning from investor behaviour to refine advice and keep portfolios aligned with personal circumstances and preferences. This AI benefit should be quite helpful. To advisers, it simply speeds up what they are doing already, under Know Your Client rules. But it will encompass a larger set of circumstances and will allow more continuous updating. This should reduce complaints, free up adviser time and allow clients to have a portfolio that better matches their circumstances, even when these are changing rapidly.
Operational efficiency and automation
AI can automate repetitive and manual tasks such as data collection, analysis and administrative processes. This frees up investment professionals’ time to focus on higher-value activities such as strategy development and client engagement, while reducing costs and operational risks. Robotic process automation (RPA) powered by AI can deliver increased efficiency and accuracy, transforming the structure and workflow of investment organizations.
These advancements are not only enhancing the effectiveness of investment decisions but are also reshaping the structure of investment firms, requiring new skill sets and fostering a more technologically integrated approach to portfolio management and client service. As AI adoption accelerates, the divide between firms that leverage these capabilities and those that do not is expected to widen, fundamentally altering the competitive landscape of the investment industry. But ultimately it is nothing to be scared of. While there certainly will be some industry disruption, the end result of new AI developments should be lower fees and better risk-adjusted returns for investors.
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