by Fabio Ruggeri, CEO & Founder – MenthorQ
A report issued on April 9, 2025, by the Bank of England’s Financial Policy Committee (FPC) cites finance as one of the many sectors benefiting from business innovations driven by artificial intelligence. The report says AI is transforming operations in the financial sector by increasing efficiency, improving decision-making, and enhancing the value of insights drawn from data.
However, the FPC report also comes with a warning. The rapid pace of AI development makes it difficult to predict the capabilities it will ultimately have and how they will be used, it explains.
One concern the report poses is the possibility that AI-driven trading strategies could backfire in a way that destabilizes markets. As more investors rely on AI-driven strategies, the report says, it could cause firms to adopt “increasingly correlated positions” and act in similar ways during market stress, both of which could amplify shocks to the economy triggered by market movements.
As startups are especially vulnerable to market crises due to their impact on investor activity, it’s valuable to understand the systemic risks AI-driven trading could introduce in the financial sector.
The danger of AI-inspired copycat trading
One of the key dangers to market stability presented by the rise of AI-inspired trading is increasingly correlated positions, which refers to more traders adopting similar strategies as they rely on similar AI algorithms. The result is less diversification, which is just as dangerous for the market as a whole as it is for individual portfolios.
The more similarity there is in market holdings, the higher the risk of localized volatility leading to widespread instability. If negative shocks lead to sell-offs in a sector that has grown more popular with traders due to the influence of AI, those sell-offs will involve a larger volume due to correlated positions. If selling is significant enough, it can lead to sharp price declines that have a destabilizing effect on the entire market.
In addition, higher correlation can lead to less liquidity during a sell-off. The higher volume of traders looking to sell will find fewer buyers, which can further depress prices and cause higher losses.
When applied to the portfolios of financial institutions, AI-driven copycat trading becomes even more dangerous. If the lack of liquidity during a downturn leads to large losses for institutions that are prevented from selling, it can threaten the stability of the entire financial system.
AI-driven trading automations also add to the threat of an AI-inspired market crisis. AI platforms can unknowingly trigger widespread instability if they are empowered to make trades based solely on data analysis without considering the broader context.
The May 2010 “flash crash” is an example of the problems AI automations can bring to markets. The event, which involved a nearly 1,000-point decline in share prices, was eventually found to be caused by an automated algorithm trading strategy used by a mutual fund.
Steps to mitigate the risks
US regulators already have rules in place that address the oversight of algorithmic trading, which generally uses computers to automate trading activity. Whether or not those rules will prove sufficient to manage the rapid evolution of AI-driven trading, however, remains to be seen.
AI introduces powerful new capabilities, allowing algorithms to consider a broader range of data, process it faster, and even provide insights supplied by predictive analytics. However, the “thinking” behind AI’s outputs is often difficult to determine, which makes it challenging to assess whether it is built upon biases or other components that could lead to faulty logic and inspire erratic market activity.
For retail and institutional traders alike, the challenge moving forward will be leveraging AI’s capabilities without suffering from its liabilities. Ongoing education can play a key role in understanding AI’s evolving implications. Retail traders relying on AI should stay engaged with communities exploring the latest developments in AI-driven trading and how to leverage them responsibly.
While AI can provide valuable insights, it can also struggle to gauge their significance in a broader context. Ultimately, allowing AI to inform trading decisions rather than make them without human oversight could be the key to preventing it from triggering the next market crisis.
Fabio Ruggeri, CEO and Founder of MenthorQ, is an expert in fintech with over 16 years of experience in enterprise business development, specializing in finance, investment strategies, and alternative data. Having worked for two major fintech multinationals and a startup in the alternative data space, Fabio has developed a deep understanding of the financial industry. Currently based between New York and Miami, he is leveraging his vast experience to lead MenthorQ, a fintech company focused on democratizing institutional-level trading through sophisticated AI-driven quantitative models.