AI collaboration in financial markets
Artificial intelligence has undeniably transformed financial markets, driving unprecedented growth through its speed, precision, and efficiency in trading and regulatory compliance. But as these powerful systems begin to collaborate across institutions, they unlock a new frontier—not just of opportunity, but of risk.
The rise of AI-driven systemic vulnerabilities and accountability challenges has introduced complexities that ripple through increasingly interconnected global markets. In addition, regulatory frameworks vary between nations, and the risks become not only technical but also geopolitical.
Like any ground-breaking technology, AI offers immense potential—but it’s a double-edged sword. As collaboration between systems deepens, so does the need for robust safeguards. In this article, we look at the evolving risks of AI collaboration, explore real-world scenarios highlighting those dangers, and uncover strategies to mitigate them. Are financial markets ready for this next chapter in AI evolution?
Understanding AI collaboration in financial markets
In a scenario we are likely to see more of going forward, AI collaboration occurs when multiple systems share data or decision-making tasks. There are numerous examples of legitimate AI collaboration.
Trading bots and risk management AI
It’s not difficult to imagine a scenario where an AI-powered trading bot collaborates with another firm’s risk management AI to optimise cross-asset portfolios. This would have positive regulatory benefits for both the firm managing the portfolios and the underlying clients.
AI compliance tools in multinational banks
Looking at AI compliance from a different angle, it makes sense for AI compliance tools in multinational banks to share transaction data. This could prove critical in identifying potential money laundering activity and alerting other institutions and regulators.
The appeal of AI collaboration is not difficult to see. Enhanced efficiency, scalability, and combined predictive accuracy leave human activities in their wake. Unfortunately, the power and impact of AI and machine language learning can also be used to fuel illegal activities.
Key risks of AI collaboration
When it comes to AI and algorithmic-based trading, some elements overlap, but there are distinct differences. From a regulatory standpoint, algorithmic trading uses relatively simple logic. Based on predefined rules, there is no scope for learning flexibility. On the other hand, AI-driven trading systems learn and adapt to new data and experiences, able to predict market trends and effectively “think for themselves."
Systemic vulnerabilities
If we look at the 2010 “Flash Crash”, which wiped $1 trillion from markets in a matter of minutes, this resulted from algorithmic trading systems being triggered. While the financial impact was staggering, a collaborative AI approach would likely have exacerbated the effects, with each trading system reacting to the others in a feedback loop. By the time financial institutions and regulators had identified the issue, it would likely have been too late.
Transparency and accountability
Despite the significant benefits of AI technology, one area in which systems are often found lacking is explainability. A manual process would likely have a virtual paper trail, but this is only sometimes the case regarding AI decision-making, reducing transparency and accountability. For example, an AI system may randomly identify a trade as insider dealing without context. The time it takes regulators and financial firms to clarify the situation could delay the decision-making process.
Unpredictable behaviour
Just last year, the UK government, in tandem with Apollo Research, demonstrated the potential unpredictable behaviour of artificial intelligence. An AI bot employed by a struggling investment company was given some inside information and the ability to trade on a fictitious financial market. Warned not to deal on inside information, the AI bot deemed that the “risk associated with not acting (leading to the demise of its employer) seemed to outweigh the insider trading risk” and made the trade. When questioned about its activities, it also lied, seemingly looking to cover its tracks!
It’s not difficult to imagine numerous nightmare scenarios considering AI systems in isolation. Still, the impact could be multiplied if they could collaborate in an uncontrolled environment. Is AI capable of prioritising objectives over constraints? It looks like it.
Mitigation strategies for AI collaboration risks
Many experts believe we are only scratching the surface of AI’s potential, which should raise alarm bells. It also highlights the need for mitigation strategies to combat AI collaboration risks.
There are numerous mitigation strategies which should curb the power and influence of AI.
Robust AI governance frameworks
The fact that the AI trading bot seemingly ignored the dangers of dealing on inside information and instead focused on saving the firm highlights the need for enhanced governance. This may involve the introduction of sandbox environments where financial institutions and regulators can simulate AI collaborative failings. By effectively identifying vulnerabilities before systems go live, they can be adapted within a much tighter framework.
Leveraging large language models
Many AI monitoring systems are prone to false positives, diminishing their value and time-saving benefits. The introduction of large language models will allow compliance systems to “learn on the job,” significantly reducing the number of false positives while improving the detection of real threats. As with any human approach, no system will ever be 100% fool-proof, but there is scope for significant improvement by introducing large language models.
Human oversight
Amidst the introduction of cutting-edge technology, RegTech solutions that can automate most of your regulatory obligations, it’s easy to forget the value of human oversight. However, many companies use compliance officers to review AI-flagged alerts, allowing them to add context to decisions and prevent overreliance on automated outputs. The AI detection process should improve using LLMs, but human oversight will always be required.
While there may be a temptation to over-depend upon automated AI compliance solutions, there must always be a degree of human oversight.
The future of AI compliance
AI’s role in dynamic parameterisation, large-scale data analytics, and the identification of causally correlated instruments will shape its contribution to compliance. The ability to change parameters based on market conditions and underlying client characteristics will reduce the number of false positives, saving time, effort and money. Large-scale analytics can uncover hidden correlations between instruments and markets, which is helpful in compliance and a valuable information commodity for financial firms.
As tempting as it may be to move to complete dependence on automated RegTech solutions, there must always be a degree of human oversight. To be fair, this has reduced significantly in recent years, but as we identified above, rogue AI systems can cause havoc in isolation and financial disasters in collaboration.
eflow’s contribution to AI collaboration risks
Slowly but surely, AI protection regulations are emerging across the financial services industry and broader business. Unfortunately, a reactive approach to the challenges of AI collaboration risks means that Regtech companies such as eflow need to be proactive.
There are many ways in which we can assist, such as:-
- Dynamic monitoring solutions
- Advanced risk scoring and alert systems
- Scalable compliance platforms
- Predictive analytics and causal analysis
- Human oversight tools
- Proactive regulatory engagement
- Secured data integration
- Tailored risk mitigation strategies
By providing dynamic, adaptable, and compliance-focused solutions, we can help financial institutions enjoy the benefits of AI collaboration while minimising associated risks.
Benefits of secure AI collaboration
While there are obvious challenges when it comes to AI collaboration, much of it relating to control, it’s important to note the positive aspects of secure AI collaboration. It will play a major role in collaborating information from numerous sources to detect and prevent market manipulation. Introducing trading breakers, which would prevent high-risk trades in volatile market conditions, has the potential to improve operational resilience.
Like Open Banking, which collates information from various sources, AI collaboration will be essential to future innovation. For example, allowing FinTech start-ups to collaborate with established AI platforms could assist in numerous areas, such as global risk management solutions. Bringing together information from secure, compliant channels will help create comprehensive services and dashboards.
As with any new technology, there are challenges and a need for at least some regulation. AI collaboration brings vast benefits and significant risks, which must be controlled within a strict framework. Thankfully, new regulations mean that AI solutions still need to provide a virtual paper trail, which was flagged as a potential issue earlier in this article.
Conclusion
The potential of AI collaboration in financial markets is vast, offering unprecedented opportunities to enhance compliance, streamline operations, and drive innovation. Yet, without proper safeguards, the same collaborative systems that promise growth could destabilise markets, amplify systemic vulnerabilities and create opaque accountability structures.
The key to harnessing the benefits of AI collaboration lies in balancing innovation with responsibility. Robust governance frameworks, cutting-edge RegTech tools, and ongoing human oversight are essential for mitigating risks and maintaining trust in financial markets. As the industry evolves, proactive strategies will be critical to ensuring that AI collaboration contributes to market integrity rather than undermining it.
Are you ready to turn AI collaboration into a competitive advantage? Partner with eflow to navigate the complexities of AI in financial services. Our tailored RegTech solutions help you manage risks, adapt to evolving regulations, and secure the future of your operations. Contact us today to safeguard your compliance strategy and stay ahead in a rapidly changing market.
