Insurance compliance teams have long relied on rules-based monitoring as the cornerstone of anti-money laundering (AML) programmes. Fixed thresholds and predefined alerts offer simplicity and auditability, but as financial crime becomes more complex, this approach is increasingly insufficient, according to SymphonyAI.
Rules-based monitoring seems effective at first glance. Legacy systems generate alerts for large premiums, early policy surrenders, or repeated claims across regions. Compliance teams review these alerts regularly, creating the impression that AML controls are robust.
However, financial criminals are agile and strategic. They design transactions to avoid thresholds, route activity across multiple jurisdictions, and exploit product or system gaps. Static rules simply cannot keep up with such dynamic behaviour.
Rules alone also fail to capture subtle patterns. The Council of Europe highlighted a case where €1m was split across two life insurance contracts, surrendered, and transferred internationally.
Individually, these actions appeared legitimate, but collectively they revealed a layering scheme undetected by traditional rules.
False positives are another challenge. UK insurers, including Allianz, faced rising motor insurance fraud involving manipulated photos, which static claim-value thresholds failed to flag. Additionally, a UK agent laundered over $1.5m through early life insurance surrenders, exploiting systems that never evolved to detect complex patterns.
AI-powered detection offers a solution. Machine learning can analyse combined behaviours, adapt automatically to new typologies, and prioritise alerts by risk.
By incorporating historical data, cross-policy activity, and regional risk factors, insurers can develop a dynamic, intelligent view of emerging threats.
Compliance teams should take five actions to enhance effectiveness: review rules, implement AI overlays, train models on real case data, integrate fraud and claims data, and ensure detection strategies continuously evolve.
Rules remain the foundation of AML, but alone they are insufficient. Pairing them with adaptive AI allows insurers to reduce false alerts, uncover hidden threats, and meet regulators’ expectations for proactive, effective compliance.
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