AI-Driven Investing: How to Use Artificial Intelligence in Your Investment

Artificial intelligence (AI) is rapidly transforming the way investors analyze markets, select stocks, and manage portfolios. With AI-driven investing tools becoming more accessible, both institutional and retail investors are leveraging machine learning algorithms, big data analytics, and automation to enhance their decision-making and optimise returns.

A recent study by Mercer found that 91% of managers are currently (54%) or planning to (37%) use AI within their investment strategy or asset class research. AI’s ability to process vast amounts of financial and alternative data in real-time allows it to detect patterns, identify opportunities, and react faster than human investors.

Unlike traditional investing models that rely on static analysis, AI-based investing platforms use adaptive learning, continuously refining their strategies based on evolving market conditions. While AI doesn’t eliminate investment risks, it serves as a powerful tool for improving efficiency, removing emotional bias, and uncovering insights that may be overlooked by human analysts.

In this article, we’ll explore the real-world applications of AI in investing, from stock selection and trade execution to portfolio management and risk assessment. Whether you’re a passive investor seeking automated wealth management or an active trader looking for AI-enhanced strategies, understanding how AI is shaping the future of investing is crucial to staying ahead in today’s data-driven markets. 🚀

How AI is shaping investment strategies

Artificial intelligence (AI) is transforming investment strategies by enhancing research, improving decision-making, and optimising portfolio management. While AI applications vary in complexity—from basic quantitative screening to advanced deep learning models—their primary function is to augment human expertise, not replace it.

Investment managers are leveraging AI to analyze alternative datasets, refine stock selection, and automate risk assessment. While some firms integrate AI as a supportive tool for research and analysis, others use it to suggest investment decisions, which human teams then evaluate. A smaller subset of firms deploys fully automated AI-driven models, but these still require human oversight to ensure accuracy and alignment with investment objectives.

Source: Mercer

AI is transforming investing, but adoption varies across different applications. Traditional techniques like quantitative screening and factor models are widely used, while generative AI and causality analysis are still gaining traction, with many firms planning future implementation. 

This trend shows that while AI-driven investing is evolving, investment managers are taking a measured approach—prioritising proven AI strategies before fully integrating advanced technologies. 

As AI capabilities improve, more firms are expected to incorporate machine learning, NLP, and generative AI to enhance investment research, risk management, and portfolio optimization.

Key AI applications in investment strategies

How do companies integrate AI applications in investment processes?

Source: Mercer

AI is transforming how investment firms analyze data, predict market trends, and manage risk. Instead of just relying on traditional financial reports, firms are now using AI to process alternative datasets like sustainability metrics, social sentiment, and satellite imagery, giving them a competitive edge in spotting trends early. 

AI is also refining predictive indicators and market analysis, helping firms move beyond historical data and anticipate shifts before they happen.

Bias reduction is another key area. AI is being used to identify and correct imbalances in research and risk models, ensuring investment strategies are based on accurate, diverse data rather than skewed historical patterns. 

While AI plays a major role in monitoring portfolios and analysing scenarios, most firms still keep trade execution and asset allocation under human control, using AI as a decision-support tool rather than a replacement.

Looking ahead, investment firms are prioritising real-time monitoring, stress testing portfolios, and refining predictive models to make smarter, faster decisions. AI is not just automating processes—it’s reshaping how investment strategies are built, making them more adaptive, data-driven, and forward-looking.

How to leverage AI for smarter investing

AI isn’t just for hedge funds and institutional investors—everyday investors can also use AI to pick stocks, automate portfolios, manage trades, and optimise asset allocation. With AI-driven tools becoming more accessible, retail investors can now analyze vast amounts of data, remove emotional bias from decisions, and streamline portfolio management like never before.

Stock picking with AI

Investors face an overwhelming amount of data when evaluating stocks. AI-powered stock screeners simplify this process by scanning financial reports, technical indicators, and sentiment analysis to identify stocks that meet your investment criteria.

💡 How AI helps:

✔ Filters stocks based on fundamental and technical data (e.g., earnings reports, trading volume, market capitalization).

✔ Uses sentiment analysis to scan news and social media for bullish or bearish signals.

✔ Employs predictive analytics to assess potential future price movements.

AI-powered automated portfolios

Robo-advisors like StashAway use AI techniques to build and manage investment portfolios based on your financial goals and risk tolerance. These platforms automate asset allocation and portfolio rebalancing, making investing effortless and cost-effective.

💡 How AI helps:

✔ Creates a diversified portfolio based on your investment goals.

✔ Automatically rebalance your portfolio if asset allocations drift.

💡 Where to start:

StashAway’s General Investing offers AI-powered, globally diversified portfolios designed to optimise long-term returns while keeping risk in check.

StashAway’s proprietary Economic Regime-based Asset Allocation (ERAA®) leverages AI for data analysis to manage and adjust your portfolio based on macroeconomic conditions. Augmented by human oversight to continually create, test, and monitor the algorithms, your investments are optimised based on market realities—not short-term fluctuations.

Why StashAway stands out

✔ Personalised risk management – The StashAway Risk Index (SRI) ensures your investments won’t exceed your risk tolerance, giving you peace of mind in any market condition.

✔ Smart portfolio optimization – Instead of cherry-picking stocks, ERAA® monitors economic trends to allocate assets intelligently, balancing protection and performance.

✔ Low, transparent fees – StashAway charges 0.2% to 0.8% annually, with no lock-in periods, withdrawal fees, or hidden charges. New investors enjoy a 3-month fee waiver on up to $25,000 SGD.

Managing trades with AI

High-speed computing and algorithmic trading have transformed how investors execute trades, manage risk, and optimise buy/sell decisions. AI-powered trading tools help eliminate emotional bias, automate trade execution, and react instantly to market conditions.

💡 How AI helps:

✔ Executes trades automatically based on pre-set conditions.

✔ Manages exit strategies with stop-loss and take-profit levels.

✔ Reduces emotional decision-making in volatile markets.

Optimising your portfolio with AI

AI can help you construct a personalised, risk-adjusted portfolio using modern portfolio theory. These models analyze asset correlations, market trends, and investment goals to find the most efficient mix of assets for higher returns at a given level of risk.

💡 How AI helps:

✔ Identifies the ideal asset allocation for risk-adjusted returns.

✔ Uses machine learning to refine investment strategies based on historical performance.

✔ Monitors and adjusts your portfolio to keep it optimised over time.

Top AI tools to enhance your investment journey

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How to start investing using AI

While institutional investors leverage cutting-edge AI models, real-time alternative data, and high-frequency trading systems, retail investors now have access to a growing suite of AI-powered investment tools. 

These tools can help with stock screening, portfolio optimization, trade execution, and market analysis—but they work best as decision-support systems rather than fully automated solutions.

1. Define your investment goals

Before diving into AI-powered platforms, clarify your financial objectives, risk tolerance, and time horizon. Different AI tools cater to different strategies—whether you’re looking for short-term trading signals, long-term wealth accumulation, or automated portfolio management.

• For long-term investors: AI-driven robo-advisors like StashAway can dynamically adjust portfolios based on macroeconomic conditions.
• For active traders: AI stock screeners like TrendSpider and Trade Ideas can identify patterns, price movements, and trading opportunities in real-time.

2. Choose AI tools that fit your strategy

AI investing tools can be categorised into stock-picking platforms, automated trading bots, sentiment analysis tools, and AI-powered portfolio managers.

• If you want to pick high-potential stocks → Use Zen Investor, FINQ, or Seeking Alpha Premium to get AI-enhanced fundamental and technical analysis.
• If you prefer passive investing → Use StashAway or TuringTrader for automated, AI-managed portfolios that adjust based on economic conditions.
• If you’re an active trader → Use TrendSpider or Trade Ideas to scan the market, identify trade setups, and automate execution.
• If you want to analyze market trends → Use LevelFields to monitor regulatory filings, leadership changes, and financial news that could impact stock prices.

3. Combine AI insights with human judgment

Even the most sophisticated AI models have limitations. While AI can filter through vast amounts of data, detect patterns, and automate processes, it still lacks contextual understanding, adaptability to unforeseen events, and the ability to incorporate qualitative factors like changing consumer behavior or geopolitical risks.

• Example: AI stock screeners might flag a stock based on strong earnings and bullish technical indicators, but human investors can factor in industry trends, competitive positioning, or regulatory changes that the model may not fully capture.
• Example: An AI robo-advisor like StashAway optimises portfolios based on economic regimes, but investors still need to ensure it aligns with their personal risk appetite and long-term goals.

4. Automate routine tasks but stay in control

AI is best used for enhancing efficiency, not replacing decision-making. Consider automating parts of your strategy—such as setting AI-driven trade alerts, risk management triggers, or portfolio rebalancing—but remain actively engaged to validate AI-generated recommendations.

• Use AI for: Identifying stocks, spotting trends, scanning news sentiment, optimising asset allocation.
• Manually review: Major investment decisions, long-term strategic shifts, macroeconomic risks AI might overlook.

5. Keep learning and refining your approach

AI technology is evolving rapidly. The tools available to retail investors today—like large language models analysing earnings call transcripts or AI stock screeners filtering opportunities—were once exclusive to hedge funds and institutional traders. As AI capabilities improve and become more accessible, staying informed will help you make the most of these advancements.

How Can Investors Avoid AI Investment Fraud?

The rise of AI in finance has led to new forms of fraudulent schemes. Scammers often promote proprietary AI trading systems that claim to guarantee returns, using complex terminology to create an illusion of sophistication. 

Some unregistered investment platforms falsely advertise risk-free AI strategies, preying on investors who may not fully understand how these models work. Fraudsters have even begun using AI-generated content, including deepfake videos and automated chatbots, to impersonate financial experts and create fake investment platforms.

To avoid these scams, investors should verify platform legitimacy through regulatory bodies such as the SEC or FINRA, be wary of claims that sound too good to be true, and seek transparency regarding how an AI model makes its investment decisions.

What Kind of Financial Data Does AI Analyze?

AI models process an extensive range of financial and alternative data to assist in investment decisions. Fundamental data, such as earnings reports, cash flow, and valuation metrics, is analyzed alongside technical indicators like stock price trends and trading volume. AI also incorporates alternative data sources, including news sentiment, earnings call transcripts, satellite imagery, and consumer spending patterns.

By synthesising multiple data points, AI can identify correlations and trends faster than human analysts, but the accuracy of these insights depends on data quality and market conditions. AI’s predictive capabilities remain limited, especially during unpredictable market events such as financial crises or geopolitical disruptions.

Is AI Investing Suitable for Beginners?

AI-powered investing is accessible to beginners, particularly through robo-advisors and AI-enhanced stock screeners. Robo-advisors simplify investment management by automatically building and maintaining diversified portfolios based on an investor’s risk tolerance and financial goals. 

AI stock screeners help individual investors filter through thousands of stocks based on customised criteria, making it easier to identify potential opportunities.

While AI tools can streamline the investment process, beginners should not blindly follow AI-generated recommendations. Understanding the logic behind AI-driven suggestions and maintaining a balanced portfolio remains essential, as even the most advanced AI cannot eliminate all market risks.

Is Investing With AI Safe?

AI investing is generally safe when used responsibly, but it is not without risks. AI can improve risk management by analysing vast amounts of data and identifying potential investment opportunities. It can also help reduce emotional decision-making, which often leads to poor trading choices.

However, AI models are only as effective as the data they are trained on and may not always perform well under extreme market conditions. Overreliance on AI-generated investment strategies can lead to herd behavior, where many investors make similar moves based on AI recommendations, potentially amplifying market volatility.

Investors should view AI as a complement to traditional investing methods rather than a replacement for thorough research and strategic decision-making.

Can AI Predict the Stock Market?

AI has shown some success in identifying short-term price trends and optimising trading strategies, but its ability to predict the stock market with certainty remains limited. Machine learning models can analyze historical data to identify patterns that may indicate future price movements, but they struggle with unexpected events such as economic downturns, geopolitical crises, or sudden regulatory changes.

Some AI-driven trading models have outperformed traditional methods in controlled environments, but their real-world performance is inconsistent. While AI can enhance investment decision-making by providing data-driven insights, investors should not expect AI to guarantee returns or replace human judgment in long-term financial planning.

AI in Investing: A Game-Changer, But Not a Silver Bullet

AI is no longer a futuristic concept in investing—it’s already shaping the way portfolios are built, trades are executed, and market trends are analyzed. Even if you’re not actively using AI, many portfolio managers, hedge funds, and robo-advisors are leveraging its capabilities to optimise investment strategies.

For hands-on investors, AI-powered platforms can help analyze market trends, automate trading decisions, and manage risk more efficiently. Retail investors now have access to AI tools that were once exclusive to institutional players, making sophisticated investment strategies more accessible.

However, AI is not a magic formula for success. It should be used as a decision-enhancing tool rather than a replacement for sound investment judgment. While AI can process vast amounts of data and uncover insights that humans might miss, it cannot fully account for market sentiment, black swan events, or the nuances of economic shifts.

The key to successfully integrating AI into investing lies in balancing automation with human oversight. Investors who combine AI-driven insights with strategic thinking, risk management, and continuous learning will be best positioned to navigate the evolving landscape of AI-driven investing.