Many people believe AI is just taking off. Yet it has helped shape the finance industry since the introduction of expert systems in the 1980s. At the time, the technology helped curb human error by supporting activities such as fraud detection and financial analysis.
Now, the finance industry faces another tech revolution due to the mainstream release of generative AI tools. A 2024 Mercer study found that 91% of managers are using or plan to use AI in their investment strategies.
AI has several use cases in finance, including streamlining operations and reducing risk. That said, to focus on growth, we must look at AI’s potential to better understand the market, improve predictions and increase returns. It’s also crucial to pay attention to AI’s potential negative impacts and take steps to mitigate those risks.
How AI enhances market predictions
According to the IMF, AI-driven trading can enable faster and more efficient markets. For instance, it allows for more agile responses to market changes, such as rebalancing portfolios. Investors also can use AI data analysis to find blind spots and hidden gems earlier.
The result is a more precise investing strategy that allows them to lead the pack, not follow.
In “On the Mind of Investors,” Stephanie Aliaga, global market strategist at JPMorgan, cites three primary use cases for AI in investing:
- Supporting research by synthesizing large amounts of data
- “Coaching” on portfolio management by providing data-driven recommendations
- Optimizing trades for efficiency
As with most AI solutions, the quality of the data affects your ROI. Given that, platforms leveraging data in a smarter way present a significant opportunity for investors who use AI to inform trade decisions.
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Ethical considerations and challenges of AI solutions
One area of concern with AI-driven decision-making is algorithmic bias. This happens when a biased dataset impacts AI recommendations. If unchecked, this could cause issues such as overreactions to market volatility. It could also reduce portfolio diversity and reinforce existing social inequalities.
As such, investors must take steps to reduce bias and ensure that final decisions align with their goals. Crucial strategies for countering algorithmic bias include:
- Supporting AI models with comprehensive and recent data
- Human-in-the-loop (HITL) approach, where you use the AI model with human oversight
- Continual performance monitoring to ensure the real-world results of AI-driven recommendations match your objectives
For example, investors using the HITL approach can provide their AI tool with portfolio diversity requirements. For instance, they can use the AI model to select the top-performing companies across various industries or company sizes.
Another potential ethical consideration is AI widening the gap between institutional and retail investors. In other words, large firms with more resources could invest in more sophisticated AI solutions. This would give them a significant advantage over smaller investors.
AI platforms can avoid this issue by creating tools aimed at investors with smaller budgets.
Leveraging AI for more equitable investment opportunities
While we’re still learning the best practices for using AI to make investment decisions, avoiding the technology isn’t the solution. At the end of the day, firms are already investing in AI and will continue to do so. Thoughtful adoption is a much better approach for those who want to see a positive impact from AI.
AI-driven investment platforms have the power to level the playing field. AI tools can empower retail investors by allowing them to analyze large amounts of financial data without a team of researchers.
In other words, AI can have a democratizing effect if software companies make solutions and provide AI education tailored toward individual investors and smaller firms.
As with any new technology, it’s essential to think of the potential ethical considerations that an AI-driven solution may pose. But by approaching AI-driven investing mindfully, we can help shape AI’s impact into one that leads to more responsible and profitable decisions for investors at every level.
