Artificial Intelligence (AI) and Retail Investing: Use Cases And Experimental Research

AI is reshaping retail investing, offering both opportunities for enhanced decision-making and challenges around transparency and data quality. Research by Ontario Securities Commission (OSC).

The recent increase in the scale and applications of artificial intelligence (AI) presents a range of new possibilities and potential risks to retail investors. As such, securities regulators are striving to understand, prioritize, and address potential investor harms, while continuing to foster innovation.

The research findings presented in this report were developed by the Ontario Securities Commission (OSC) in collaboration with the Behavioural Insights Team (BIT) as part of the OSC’s evidence-based approach to regulatory and educational initiatives. Our findings stem from two research streams. We conducted a literature review and environmental scan of investing platforms to understand the prominent use cases of AI systems that are retail investor-facing. We then used the findings from this research to inform the design and implementation of a behavioural science experiment to determine how the source of an investment suggestion – AI, human, or a blend of the two – impacts the extent to which investors follow that suggestion.

Based on the literature review and environmental scan conducted in our first research stream, we identified three broad use cases of AI specific to retail investors:

Decision Support: AI systems that provide recommendations or advice to guide retail investor investment decisions.^[1]
Automation: AI systems that automate portfolio and/or fund (e.g., ETF) management for retail investors.
Scams and Fraud: AI systems that facilitate scams and fraud targeting retail investors, as well as frauds capitalizing on the “buzz” of AI.

Within these use cases, we identified several key benefits and risks associated with the adoption and usage of AI systems by retail investors, including the following.

Benefits:

Reduced Cost: AI systems can reduce the cost of personalized advice and portfolio management, thereby creating considerable value for retail investors.^[2]
Access to Advice: More sophisticated and properly regulated AI systems can provide increased access to financial advice for retail investors, particularly those that cannot access advice through traditional channels.
Improved Decision Making: AI tools can be developed to guide investor decision-making around key areas such as portfolio diversification and risk management, as well as tools to assist investors in identifying financial scams.^[3]
Enhanced Performance: Existing research has shown that AI systems can make more accurate predictions of earnings changes and generate more profitable trading strategies compared to human analysts.^[4]

Risks:

Bias: AI models are generally subject to the biases and assumptions of the humans who develop them. As such, they may heighten unfair outcomes, even where this is not the system’s intended function.
Herding: The concentration of AI tools among a few providers may induce herding behaviour, convergence of investment strategies, and chain reactions that exacerbate volatility during market shocks.
Data Quality: If an AI model is built on poor data quality, then the outputs, whether advice, recommendations, or otherwise, will be of poor quality as well.
Governance and Ethics: The ‘black box’ nature of AI systems and limitations around data privacy and transparency create concerns around clear accountability in cases where AI systems produce adverse outcomes for investors.

Our second research stream consisted of implementing an online, randomized controlled trial (RCT). We tested how closely Canadians followed a suggestion for how to invest a hypothetical $20,000 across three types of assets: equities, fixed income, and cash. We varied who provided the investment suggestion: a human financial services provider, an AI investment tool, or a human financial services provider using an AI tool (i.e., ‘blended’ approach). We also varied whether the suggested asset allocation was sound or unsound to see whether Canadians could discern the quality of the suggestion depending on who was delivering it. Table 1 outlines the different variations of investment suggestions we tested.[…]