Compliance teams across the financial sector are grappling with overwhelming volumes of communications. From emails to messaging apps, the daily flood of interactions now far exceeds the capacity of human reviewers. Yet regulators are demanding more vigilance than ever before.
Artificial intelligence has emerged as a vital tool to ease this burden, but some firms are misstepping by either relying entirely on automation or steering clear of it altogether. Neither route meets regulatory expectations, claims Saifr.
The most defensible approach combines automation with human oversight, using AI for its strengths in aggregation and pattern recognition while reserving contextual judgment for compliance officers.
Automation on its own has limits. AI can flag thousands of keywords in seconds, but it cannot reliably distinguish between an adviser discussing legitimate estate planning and someone hinting at insider trading. U.S. regulators such as FINRA, through Rule 3110, stress that firms must have supervisory systems “reasonably designed” to ensure compliance. The SEC’s recent roundtable on AI echoed that human validation remains essential. A flagged keyword like “gift cards” may look suspicious to an algorithm, yet a human can see that it refers to approved client holiday bonuses.
Firms successfully blending AI with human review often begin with technology as the first line of defence. AI is adept at scanning communications across multiple channels, detecting not only keywords but sentiment, relationships and unusual patterns that may point to breaches. This can be especially powerful in anti-money laundering (AML) and know your customer (KYC) checks, where algorithms can spot references to sanctioned jurisdictions, unusual transactions, or deviations from account opening protocols.
Humans, however, remain the final arbiters. Compliance professionals are needed to investigate flagged messages, evaluate intent and apply firm policies. For instance, AI might highlight an adviser asking about a client’s source of funds, but only a human can determine whether this is proper enhanced due diligence or an intrusive step that risks client trust.
The benefits extend beyond traditional surveillance. Communications data can help strengthen AML and KYC monitoring, with AI scanning for signs of politically exposed persons or beneficial ownership concerns. But here too, human judgment is required to interpret context and escalate genuine risks.
Marketing is another area where human-AI collaboration is critical. FINRA Rule 2210 places obligations on marketing communications, where errors can result in heavy penalties. AI can speed reviews by spotting prohibited terms or missing disclosures, but human reviewers must assess whether claims are fair and balanced, or if materials risk misleading investors.
Ethical concerns around AI bias also loom large. Leading firms mitigate this by documenting how models operate, conducting regular testing, and ensuring all alerts undergo human review before action. Escalation protocols and robust audit trails further strengthen accountability. Regulators have made clear that fiduciary duties remain intact regardless of technology, meaning humans cannot be removed from the process.
Building effective programmes requires structured governance, staff training, and clear escalation pathways. Firms must also update systems regularly to reflect new risks and regulatory changes. Done right, this model offers scalability without sacrificing quality, reduces false positives, and demonstrates regulatory confidence.
As one compliance director remarked, “AI doesn’t replace our judgment—it amplifies it.” The future of surveillance lies in this collaborative intelligence model. By integrating AI into human-centred compliance processes, firms create systems that neither technology nor humans alone could achieve, protecting both clients and the organisation.
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