Static, rules-based monitoring has traditionally formed the backbone of insurance anti-money laundering (AML) programmes. For years, compliance teams have relied on fixed thresholds and hardcoded rules to detect suspicious activity. While this approach provides auditability and regulatory reassurance, it is no longer sufficient in the face of modern financial crime, according to SymphonyAI.
The misconception that rules capture all AML risks remains widespread. Legacy systems flag large premiums, early policy surrenders, or duplicate claims, giving teams the impression that alerts equate to coverage. Yet this approach ignores the evolving tactics of financial criminals.
Criminals are adaptive and sophisticated. They exploit gaps in products, policies, and geographies, routing funds to stay below alert thresholds.
Rules-based systems are unable to detect complex, coordinated behaviour, which is increasingly the hallmark of modern laundering schemes.
For example, a case reported by the Council of Europe involved an individual depositing €1m into two life insurance contracts, surrendering both, and transferring the proceeds abroad. Each step appeared legitimate alone, but together they constituted a layering scheme invisible to static rules.
High false positives exacerbate the problem. UK insurers, including Allianz, encountered rising motor insurance fraud using doctored photos, which rigid thresholds failed to catch. Similarly, an insurance agent laundered $1.5m over several years by exploiting static life insurance processes, highlighting the system’s failure to evolve.
AI and machine learning offer a path forward. These technologies detect risk based on behavioural combinations, adapt to new typologies, and prioritise alerts based on real risk scoring. By incorporating case history, regional trends, and cross-policy activity, insurers gain a dynamic, constantly updating view of threats.
Compliance teams can improve effectiveness by auditing rules, adding AI overlays, training models on real data, integrating cross-domain signals, and continuously evolving monitoring practices.
While rules form the foundation of compliance, they alone are reactive and limited. Pairing them with AI allows insurers to reduce false positives, detect hidden threats, and satisfy regulators’ focus on true AML effectiveness.
Read the full blog from SymphonyAI here.
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