We can’t go a single day without hearing how artificial intelligence (AI) is turning daily life upside down, and even the world of finance isn’t sitting this one out.
But before we blanket everything in AI, let’s cover the basics, starting with what AI actually means in investing, then tackle how both retail and professional investors are putting the technology to work.
What does AI mean in investing?
For our purposes, we’ll use AI to describe three aspects of investing:
- Automated investing and portfolio management: Think of robo-advisors like those offered by Fidelity or Vanguard. These platforms use algorithms to build and manage diversified portfolios based on users goals, time horizon and risk tolerance—automating a small part of what a human advisor would typically do.
- Large language models (LLMs): Think ChatGPT or Claude, which can process massive amounts of information—especially unstructured data such as news articles, earnings calls, or financial reports—in seconds. They help identify trends, sentiment, and insights that would normally take a human hours to do.
- Advanced algorithms and machine learning: These systems analyze historical market data and run complex simulations to forecast trends or assess risk, ultimately producing models that could help investors make more data-driven decisions.
Robo-advisors: The original AI investors
Building and managing a diversified portfolio is the key to long-term success, and many believe AI is making that process more efficient and personalized than ever before. Robo-advisors are the clearest example of this technology in action.
Today, most of the largest brokerages, companies like Fidelity, Vanguard, Schwab, Wells Fargo, and others, offer a robo-advisor option.
These platforms use algorithms to create and manage portfolios based on an investor’s goals, risk tolerance, and time horizon. It’s usually as simple as answering a few questions; from there, the robo-advisor builds a diversified portfolio, often using low-cost exchange-traded funds (ETFs).
The benefits are clear:
Lower costs: Robo-advisors typically can charge lower fees than traditional human financial advisors.
Accessibility: You can start investing with a small amount of money, bringing professional-grade portfolio management to everyone.
Automatic rebalancing: Typically this type of system automatically adjusts holdings to stay aligned with your goals. When one asset class outperforms, it should sell some and buy more of what that system considers to be lagging to maintain its definition of a balance.
This hands-off approach can help investors stay consistent and avoid the emotional decision-making that often derails long-term investing.
More efficient analysis with LLMs
One of the most powerful uses of modern AI in investing comes from large language models (LLMs). Think tools like ChatGPT or Claude that can read, understand, and interpret massive amounts of text in seconds. Humans can only process so much information at once, but LLMs can simultaneously sift through market data, news articles, earnings reports, and even social media sentiment to uncover patterns and signals that might slip past human analysts.
For example, an AI tool can scan thousands of news stories about a specific industry to gauge overall market sentiment. It can detect subtle shifts in tone or language that might suggest a company could potentially be heading for a strong quarter—or, conversely, facing trouble ahead. This makes a far more data-driven approach to stock picking possible.
Think of it as having a team of tireless analysts working around the clock, combing through every headline, filing, and earnings call transcript. These tools can flag stocks that show unusual trading activity, shifting sentiment, or fundamentals that align with your investment criteria—potentially surfacing opportunities faster than any human team ever could.
Smarter analysis with machine learning
Every investment comes with some level of risk—the key is understanding and managing it effectively. Machine learning (ML) has become a powerful tool in that process, helping investors identify patterns, forecast volatility, and anticipate potential pitfalls before they materialize.
Unlike traditional risk models that rely mostly on historical averages, machine learning algorithms can analyze vast amounts of market data, uncover subtle correlations, and update their models in real time. For example, an ML system might detect how small changes in interest rates consistently ripple through specific sectors like technology or real estate. When it spots those relationships shifting, it can help flag potential risks before they show up in portfolio performance.
While no person or no AI method can predict future performance with absolute certainty, this forward-looking capability is a major leap from conventional methods. It’s not just about hopefully avoiding losses—it’s about making smarter, data-driven decisions. With a clearer picture of potential downside scenarios, investors can potentially adjust their strategy early, whether that means diversifying holdings, rebalancing exposure, or moving into more defensive positions.
Benefits and challenges of AI in investing
Whatever path you choose, just know that AI isn’t a magic bullet for investing. It’s got its perks and its pitfalls
Benefits:
Improved decision-making: AI provides data-driven insights that can help remove emotional bias from investing.
Greater efficiency: Automated processes like portfolio rebalancing and data analysis save time and effort.
Increased accessibility: Tools like robo-advisors have democratized investing, making sophisticated strategies available to everyone that has access to those advisors.
Challenges:
Over-reliance on algorithms: Markets can be unpredictable, and black swan events that AI has never seen before (like a global pandemic) can challenge models.
Data privacy: Most AI investing tools will ask for you to share personal financial data, so be sure to use reputable platforms with strong security protocols.
Complexity and "black box" issues: Some AI models are so complex that even their creators don't fully understand how they came to a conclusion. This lack of transparency can be concerning for investors who want to know the "why" behind every decision.
The takeaway
AI offers exciting possibilities to enhance your investment strategy, but like a lot of new innovations, you don’t want to hand over the keys and walk away. Even if technology does most of the heavy lifting, it’s still important to understand what’s happening and why.
Here are a few ways to make AI work with you, not for you:
Consider robo-advisors: If you’re new to investing or prefer a hands-off approach, robo-advisors can be a great way to build a diversified, low-cost portfolio. Do your research and choose a platform that aligns with your objectives.
Use AI-powered research tools: Many brokerage platforms offer AI-driven tools that can help screen for stocks, analyze market sentiment, and identify trends. Use these insights to supplement your own research.
Stay informed but critical: Understand the basics of how AI tools work. View any tool that promises guaranteed returns with a healthy dose of skepticism. Remember, AI is a powerful assistant, but you are still the portfolio manager.
Embrace these tools responsibly, and you should be better equipped to make informed, data-driven decisions that help keep your portfolio—and your confidence—on solid ground.
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