There’s no denying AI’s ever-increasing presence across, well, absolutely everything. From transportation to telecommunications, the emerging field is totally transforming (or at least notably altering) the way things have always been done. One area that certainly isn’t immune is investing. New tools, new platforms and new technologies are shifting how investors research, trade and manage their investments.

Curious about how AI could inform your own investing strategy? We’re exploring how it’s currently being used, plus some potential opportunities and drawbacks to consider before adopting it.

A quick note: while we’re obviously talking about AI's benefits, we’re not necessarily endorsing it or advising you to integrate it into your investing journey. Instead, we’re just presenting some findings so you can decide for yourself.

A quick refresher: what is AI?

AI stands for ‘artificial intelligence’, and it’s effectively a branch of computer science whereby machines perform tasks that humans usually handle. AI works by analysing huge amounts of data to make decisions or predictions. This data could be numbers, written text, images, videos, symbols and so on.

Within the field of AI, there are several ‘sub-technologies’. One is machine learning, where models can effectively learn by themselves. Another is computer vision – AI that can process visual mediums like images and videos. Another still is natural language processing, which is AI that works with text or spoken language. Each has different capabilities and is used for different tasks – some of which we’ll go through shortly.

The history of AI’s role in investing

While it might seem that tech/AI and investing have only really intersected in recent years, their relationship goes back many decades – to the 1970s in fact.

Then, many financial institutions were using fairly basic applications that could execute automatic trades based on pre-determined criteria. At the time, though, they only relied on rudimentary technologies like basic statistical models.

Over the years that followed, these systems got better and better, with new types of tech entering the fold. For instance, the 1990s introduced AI technologies such as neural networks, which improved pattern recognition and accuracy.

The 2000s then saw the introduction of machine learning and data processing. AI-backed algorithms could also execute a new approach known as high-frequency trading (HFT), which saw algorithms implement trades in fractions of a second.

Since then, other AI technologies have emerged – including those that can go deeper than just number crunching and trading. Some tools can even analyse news articles and social media platforms to extract insight into investor sentiment and market predictions.

11 of AI’s current use cases

AI is being used across several stages of investing, from research all the way to portfolio management. Here are some of the ways both institutional and retail investors are implementing it.

1. Investing research: Research often involves wading through pages upon pages of company documents, news articles, market data and financial reports. But AI can streamline the investing research phase by extracting the most important points and insights almost instantly
2. Robo-advisers: As the name might suggest, these are effectively robotic financial advisers. They provide guidance on investments, portfolio management and financial planning, and can also execute trades on an investor's behalf. Even though there’s no human involved in the process, robo-advisers do personalise their experience to a degree. They can take into account an investor’s financial goals, risk tolerance and investing horizon before delivering suggestions and strategies
3. Algorithmic trading: This is one of the biggest areas where AI is having an impact. In fact, the algorithmic trading market is already worth billions and is expected to blow up even more in the coming years. Algorithmic trading can buy or sell almost instantly (well, milliseconds to be exact), allowing investors to capitalise on the tiniest price differences. The idea is that it can execute an order with minimal error and remove the emotion often associated with trading
4. Predictive analytics: Predictive models analyse historical data to forecast future trends in the market, asset prices, company performance and economic indicators, helping investors figure out potential investment opportunities. (Here it’s absolutely worth mentioning the classic investing caveat: past performance isn’t an indicator of future performance – even if advanced robots say it is!)
5. Sentiment analysis: Market sentiment – i.e. the overall mood of the market – is a huge factor in investing because it influences investor behaviour. Using natural language processing, sentiment analysis technology can dive into text-based content like news articles and headlines, financial reports, social media and online forums. It then tries to figure out how investors are feeling – and how the market might react
6. Portfolio management: Some AI tools can maintain your portfolio. They might optimise asset allocation, automatically rebalance your investments, assist with risk management, look for investing opportunities, and deliver insights and recommendations. They may also handle some of the more mundane tasks associated with portfolio management – like generating tax reports
7. Financial planning: AI-driven applications can assist with budgeting , financial forecasting and banking. While these kinds of tools aren't necessarily investing-specific, they can support overall financial management
8. ESG investing: For those looking to specifically invest in ESG shares or ETFs , AI models can analyse different companies’ ESG performance according to specific criteria. These might look at their environmental, social and other initiatives, and then identify investment opportunities to suit
9. Investor support: Tools like chatbots and virtual assistants can provide instant customer support, answer certain queries and help with account management
10. Fraud detection: While this is more beneficial for financial institutions like banks and insurance companies, AI-driven fraud detection is becoming incredibly valuable. Some models can look for unusual trading patterns and anomalies that could signal fraudulent or suspicious activities, and then flag those findings to security teams
11. Market surveillance: Once again this is more of an industry benefit, but there’s a growing number of tools that can monitor trading activity. Their goal is to detect things like insider trading and regulatory non-compliance

The potential benefits of AI

AI can offer potential benefits across many stages of the investing journey:

• It could provide a more accurate way to analyse market data: AI can process huge amounts of data in next to no time. It’s then able to analyse this data and look for patterns or trends at a practically microscopic level that may not be perceptable to humans. This means it has the potential to offer better accuracy, although its limits are still being established
• It can remove the emotion from investing: Making rational decisions as an investor isn’t always easy, but AI could provide more objective insights. This may help you make more practical choices that aren’t driven by emotions
• It may help you define your investing strategy: If you’re struggling to pinpoint your risk tolerance or optimal asset allocation, AI may be able to provide useful insights. It could detect patterns in your behaviour or investing preferences that aren't as obvious to you
• It can offer access to advanced tools: Previously, a lot of applications were only accessible to large firms and institutional investors. However, there’s now a suite of AI tools that any investor can take advantage of
• It can automate mundane tasks: Rather than spending your time organising your taxes or staying on top of news updates, AI could do them for you. This could allow you to focus on the more strategic aspects of your investing journey

And the possible drawbacks

Naturally, AI also has several possible limitations and challenges to think about:

• AI can (and does) get it wrong: Despite its immense capabilities, AI isn’t a foolproof solution. It often misinterprets data and makes mistakes, which is why it’s never wise to rely on it completely. AI technologies are really only as good as the data they’re trained on, and that data could have inaccuracies
• AI can be biased: Because AI uses historical data that may be skewed in a certain direction, it’s subject to biases as a result. This may lead to investing decisions that are heavily weighted towards particular trends or patterns
• It comes with possible ethical and regulatory issues: AI still sits in something of a regulatory grey area. Its use hasn’t yet been controlled and government bodies are still playing catch-up . As such, there’s still the possibility that AI can be used in nefarious ways, like market manipulation
• Using AI can lead to over-reliance: One of AI’s primary purposes is to streamline many of the tedious tasks associated with investing – research, admin and so on. So, it’s completely understandable that its efficiency could cause someone to depend on it too much. We know AI can make mistakes, so solely relying on it for investing decisions could lead to misinformation or poor investments

Tips for using AI to invest

Before you integrate AI into your investing, consider these tips. AI has huge potential, but it pays to approach it with caution (and an arsenal of research!):

• Figure out what you need: Determine your investing goals and figure out how AI could fit into those. Do you need AI to analyse the market, conduct research, manage your portfolio or automatically execute your trades?
• Start small: If you’re new to AI technology, you could start with simpler applications that do the basics like research or admin. Over time, and if and when you’re comfortable, you could consider more complex technologies, such as those that execute trades
• Compare tools and platforms: There are so many options out there, and as AI technology gains even more momentum, the selection of tools available will only grow. Look at the features of each tool or platform, assess their useability and how they can integrate with your existing applications
• Keep tabs on your investments: While AI can take care of certain tasks, human oversight can’t be underestimated. Avoid leaving your AI applications to their own devices, as doing so could lead to those errors we mentioned earlier. Check in on your portfolio regularly and do your due diligence to ensure your investments are on track and aligned to your goals
• Always do your own research: In a similar vein, relying only on AI-driven recommendations could lead to poor investing decisions. Use it for preliminary research then verify any key findings by undertaking research yourself . And if there’s ever any uncertainty or an area that seems unclear, consider doing your own research or chatting to a financial adviser. It’s always crucial to understand exactly what you’re investing in
• Remember that you can invest in AI, too: This isn’t explicitly a way to integrate AI into your investing strategy , but it bears mentioning. There are several ETFs and individual stocks that offer exposure to AI, allowing you to put your money into its continual growth. As with any other asset, these shares and ETFs offer both opportunities and risks

AI is an exciting game-changer for many industries, including investing. There are so many ways it can be used that could offer huge benefits to investors, especially helping to streamline many of the menial tasks that come with the territory. But, it also has possible risks – and it pays to be aware of them before you use it.

Remember that AI can’t yet replace the expertise and oversight of a human. So, if you’re ever unsure about your strategy or investments, think about reaching out to a licensed financial adviser for guidance.

Happy investing!