The Role of AI in Market Surveillance

In the rapidly evolving financial sector, the role of Artificial Intelligence (AI) has become significant, particularly around market surveillance. With the rapid advancement of technology and the growing complexity of trading activities, traditional surveillance methods are struggling to keep pace with this evolution. AI can enhance the efficiency of market surveillance but also to introduce new capabilities that were previously unthinkable.

Traditional Surveillance: The Need for Innovation

Historically, market surveillance has been primarily rule-based, relying on pre-specified rules and thresholds to detect potential market manipulation. These systems have been effective to a degree but come with significant limitations. For instance, rule-based systems often generate a high volume of false positives, leading to inefficiencies and increased operational costs. Moreover, they struggle to adapt to new and evolving market conditions, as they are designed to detect only known patterns of behavior. This lack of adaptability makes them less effective in identifying new and emerging forms of market abuse.

The need for more dynamic and robust surveillance systems is widely acknowledged among financial institutions and regulators. The limitations of traditional methods have created a demand for innovative solutions that can handle large and complex datasets, adapt to changing market conditions, and provide more accurate detection of suspicious activities. In September of 2023, United States Securities and Exchange Commission (SEC) Chair Gary Gensler testified in a Senate oversight hearing that the SEC was currently using artificial intelligence (AI) technologies to monitor the financial sector for signs of fraud and manipulation. AI technologies could strengthen the SECs abilities to execute market surveillance, perform disclosure reviews, and conduct economic analysis.

AI and Machine Learning: Revolutionizing Market Surveillance

AI through machine learning (ML), offers a transformative approach to market surveillance. Unlike rule-based systems, AI can learn from data, identify patterns, and make predictions with minimal human intervention. This capability is crucial in an environment where trading strategies and market conditions are constantly changing.

AI-driven surveillance systems can process vast amounts of data from multiple sources, including trading platforms, news feeds, and internal records. This allows for real-time analysis and the detection of complex trading patterns that may indicate market manipulation that in the past with tradition surveillance would not be visible. For example, AI can identify unusual trading volumes or price movements that are not aligned with historical patterns, providing early warnings of potential market abuse.

Moreover, AI can significantly reduce the volume of false positives by refining alert thresholds and parameters based on historical data and market conditions. This not only enhances the accuracy of surveillance but also allows compliance teams to focus on genuinely suspicious activities, improving overall efficiency.

The Benefits of AI in Market Surveillance

The integration of AI into market surveillance brings several key benefits:

• Adaptability and Scalability: AI-based models are highly adaptable and can scale to handle large datasets and diverse market scenarios. This flexibility is essential for monitoring various asset classes and adapting to new market developments.
• Real-time Anomaly Detection: AI can analyze trading data in real-time, identifying patterns and anomalies that may indicate manipulative behavior. This capability allows for more proactive surveillance and quicker responses to potential threats.
• Reduction of False Positives: By refining alert systems and incorporating feedback loops, AI can drastically reduce the number of false positives, allowing surveillance teams to focus on genuine risks.
• Enhanced Predictive Capabilities: AI’s ability to learn from historical data and identify patterns enables it to predict future market behavior, providing valuable insights for risk management and compliance efforts.

Challenges and Considerations

While the potential of AI in market surveillance is immense, it is not without challenges. One of the primary concerns is the transparency of AI models, which can make it difficult for non-technical users to understand the decision-making processes. A good example of this could be found in the assessment of credit worthiness of an applicant. Financial institutions use various data points, including credit history, income, employment status, and other financial behaviors in an algorithm that can sometimes appear as a “black box” to the applicant.  This “black box” decision-making process could have legal implications like bias, which could lead to violations of anti-discrimination laws. To address this, organizations must focus on improving the transparency and explainability of AI systems, ensuring that they are auditable and open to inspection.

Additionally, the successful implementation of AI in market surveillance requires a robust technical infrastructure and skilled human capital. Organizations must invest in both technology and training to fully realize the benefits of AI while maintaining compliance with regulatory requirements.  The SEC is just one of many agencies that are proposing new requirements for firms to analyze conflicts of interest that may emerge with the use of predictive data analytics and may put the interest of the firm ahead of the their investors. 

The role of AI in market surveillance is set to expand as financial markets continue to evolve. By offering enhanced adaptability, real-time analysis, and improved accuracy, AI has the potential to revolutionize how market manipulation is detected and prevented. However, to fully harness this potential, organizations must navigate the challenges associated with AI implementation, including transparency and the need for specialized skills. As the financial industry moves forward, AI will undoubtedly play a pivotal role in safeguarding market integrity and protecting investor confidence.

To learn more about how Veritas can help you with communication surveillance check out this infographic.