Artificial intelligence (AI) is increasingly shaping the compliance and surveillance landscape, offering powerful tools to identify risks, streamline processes, and enhance regulatory oversight. However, as AI adoption grows, firms must navigate critical challenges to ensure effective implementation and usage, according to Travis Schwab, CEO of Eventus.
One of the biggest barriers to AI adoption in compliance is ensuring that AI is applied to specific business functions. Speaking on the sidelines of FIA Boca in Florida, Schwab said that AI is not always suitable for every task.
“You don’t want to put AI in a position that doesn’t work for the business issue you are trying to solve. If you need a discrete, repeatable answer, using AI is a mistake. You could get a non-deterministic answer—meaning you might receive a different response even when asking the same question,” he told Traders Magazine.
Beyond proper issue applicability, security concerns and data integrity are also major obstacles. Schwab highlighted the importance of protecting sensitive data while ensuring the quality of data used for AI training: “Security and data governance are huge concerns. You have to make sure you’re using the right data to train the AI and that it remains protected within your tech stack,” he said.
Despite these challenges, AI provides significant advantages in compliance monitoring and trade surveillance. One of AI’s key strengths is its ability to process vast amounts of data and uncover hidden patterns that human analysts might overlook or not even consider in the first place.
“Instead of having an analyst ask broad questions and search for random anomalies, AI can do that work automatically. It can identify patterns and practices that you might not even know to look for and highlight behaviors that deviate from the norm,” Schwab said.
Additionally, AI enhances the efficiency of alert resolution by filtering and prioritizing alerts: “AI is great at bubbling up actionable alerts while automatically closing or deprioritizing non-actionable ones. This helps compliance teams focus on what really matters, rather than getting lost in an overwhelming number of false positives.”
As AI adoption accelerates, there is an ongoing debate about whether regulators should introduce AI-specific compliance rules. Schwab believes that existing regulatory frameworks are sufficient: “There’s no need for a separate set of AI regulations.”
“The same fiduciary and vendor responsibilities apply, whether you use AI or not. That said, regulators should provide guidance and best practices to ensure responsible AI adoption,” he stressed.
However, if regulators fail to keep pace with AI-driven compliance, inefficiencies could arise, he noted: “Regulators are always playing catch-up to the market, which moves faster due to financial incentives. If they don’t adapt, they risk being overwhelmed by the sheer volume of data, making it harder to monitor bad actors effectively.”
For compliance platforms to succeed in today’s fast-changing environment, flexibility is essential. Schwab emphasized the importance of adaptable technology: “Modern platforms must be flexible and able to integrate new technologies seamlessly. Many legacy systems are too rigid, making it difficult for firms to evolve with the market.”
As AI-driven compliance is becoming a foundational industry capability, firms must implement it methodically to ensure explainability and auditability:
“You must be able to explain to the regulators why any and all actions are taken in your compliance and surveillance platform. If the AI makes decisions or takes action without complete explainability, it can put the firm in a far worse position than if they hadn’t used the technology in the first place,” he said.
Schwab also highlighted that data quality is a fundamental requirement for effective AI implementation. He pointed out that firms must establish a robust reconciliation process with their “golden sources” of data:
“Trade surveillance platforms rely on constantly changing data feeds. Firms need to reconcile their golden data sources with their surveillance systems. If a trade isn’t correctly reflected across both, you have to stop and investigate,” he said.
Without strong data governance, AI’s effectiveness is severely limited. Schwab stressed that this process is iterative and requires continuous validation: “There’s no magic wand. You have to continuously test and refresh data integration processes as markets and the business evolves.”
While AI remains a transformative force, automation is another critical technology enhancing compliance workflows. Schwab sees automation as a stepping stone toward AI adoption: “Automation is playing a key role in streamlining compliance. It helps reduce manual effort and enhances efficiency, serving as an important step before full AI adoption.”
AI is already reshaping compliance and trade surveillance, but successful implementation requires careful planning, robust data governance, and regulatory alignment. As Schwab highlighted, AI’s ability to detect patterns, streamline alerts, and improve efficiency makes it a powerful tool. However, firms must remain deliberate in their approach, ensuring transparency and security to maximize AI’s potential in compliance.
