Earlier this month, the Ontario Securities Commission (OSC) released a report on research performed in conjunction with the Behavioral Insights Team (BIT) into the role that artificial intelligence (AI) plays in supporting retail investor decision making. The experiment and research performed is in line with the OSC’s mandate to provide protection to investors from unfair, improper or fraudulent practices and to contribute to the stability of the financial system and the reduction of systemic risk.
The OSC collaborated with the 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.
To do this, the teams used two research streams:
- A literature review and environmental scan of investor-facing AI systems to identify the current use cases of AI that are retail investor-facing; and
- A behavioural science experiment to determine how the source of an investment suggestion (AI, human, or a combination) impacts the extent investors follow that suggestion.
Based on the literature review, the report identified three broad use cases of AI which are specific to retail investors, being decision support (where systems provide recommendations to investors), systems that automate portfolio management, and systems that facilitate scams and fraud.
The report highlighted some benefits associated with these use cases, including reduced cost of personalized advice, increased access to financial advice, and improved decision making in areas such as portfolio diversification and risk management.
The report highlighted some benefits associated with some of these use cases, including reduced cost of personalized advice, increased access to financial advice, and improved decision making in areas such as portfolio diversification and risk management.
The second part of the experiment looked at hypothetical investment suggestions for three types of assets: equities, fixed income, and cash. The investment suggestions came from a human financial services provider, AI investment tool and a human financial services provider using an AI tool. The team found that people who received investment suggestions from a human using an AI tool followed that suggestion more closely that the other two sources. Although, this was a scenario using hypothetical money, the results may vary in a real-world setting. Readers of the report were strongly cautioned that the differences shown did not meet the testers’ stringent statistical thresholds. It was noted that there is an ongoing need to better understand the provision of investment recommendations from AI systems:
“In particular, there is a need to ensure that algorithms are based on high quality data, that factors contributing to bias are proactively addressed, and that these applications prioritize the best interests of investors rather than the firms who develop them.”
If you have any questions on the report, or how your firm can start to formulate AI policies, please contact us.
September 27, 2024