AI in Retail Investing: Growth, Prospects & Hurdles

Published Date: 11/09/2024

Artificial intelligence is transforming the retail investing landscape, offering benefits such as reduced costs, improved decision-making, and enhanced performance. However, it also poses risks such as bias, herding, and data quality issues.

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 Ontario Securities Commission (OSC) collaborated with the Behavioural Insights Team (BIT) to provide a research-based overview of the current use cases of AI within the context of retail investing, and the effects of AI systems on investor attitudes, behaviours, and decision-making.

Our research identified three broad use cases of AI specific to retail investors  Decision Support, Automation, and Scams and Fraud. Within these use cases, we identified several key benefits and risks associated with the adoption and usage of AI systems by retail investors.

The benefits of AI in retail investing include reduced costs, improved decision-making, and enhanced performance. However, there are also risks such as bias, herding, and data quality issues.

To address these risks, 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.

Our experiment found that people who received the investment suggestion from a human using an AI tool (i.e., a “blended” advisor) followed the suggestion most closely. However, these findings should be interpreted with caution, as the differences were not large enough to meet our stringent statistical criteria.

In conclusion, AI has the potential to transform the retail investing landscape, but it also poses significant risks. Regulators and industry participants must work together to ensure that AI systems are designed and implemented in a way that prioritizes investor protection and well-being.

Decision Support

AI systems can provide recommendations or advice to guide investment decisions. This includes applications that provide advice directly to retail investors and those that help individual registrants provide advice to their retail investor clients.

Automation

AI systems can automate portfolio and/or fund (e.g., ETF) management for retail investors. Unlike decision support, these AI applications require minimal user input, making investment decisions for investors instead of providing advice and letting the investor decideScams and FrauAI systems can also be used to enhance scams and fraud targeting retail investors, as well as generate scams capitalizing on the “buzz” of AI.

Experimental Research

Our experiment 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).

AI has the potential to transform the retail investing landscape, but it also poses significant risks. Regulators and industry participants must work together to ensure that AI systems are designed and implemented in a way that prioritizes investor protection and well-being.

FAQS:

Q: What are the benefits of AI in retail investing?

A: The benefits of AI in retail investing include reduced costs, improved decision-making, and enhanced performance.

Q: What are the risks of AI in retail investing?

A: The risks of AI in retail investing include bias, herding, and data quality issues.

Q: What are the three broad use cases of AI in retail investing?

A: The three broad use cases of AI in retail investing are Decision Support, Automation, and Scams and Fraud.

Q: What was the finding of the experiment conducted by the OSC and BIT?

A: The experiment found that people who received the investment suggestion from a human using an AI tool (i.e., a “blended” advisor) followed the suggestion most closely.

Q: What is the conclusion of the research on AI in retail investing?

A: AI has the potential to transform the retail investing landscape, but it also poses significant risks. Regulators and industry participants must work together to ensure that AI systems are designed and implemented in a way that prioritizes investor protection and well-being.

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