For years, rules-based monitoring has been the backbone of anti-money laundering (AML) programmes for insurers. Static detection frameworks, built around fixed thresholds and predefined conditions, have offered familiarity and regulatory comfort. Yet, in today’s evolving financial crime landscape, the belief that rules alone are enough is rapidly losing ground, according to SymphonyAI.
The myth that rules-based monitoring covers all essentials persists across the industry. Many legacy AML systems trigger alerts when premiums exceed a set amount, policies are surrendered early, or duplicate claims appear across regions. These rules are rarely updated and provide a sense of security as long as alerts are being investigated.
But criminals don’t follow static patterns. They are adaptive, using sophisticated tactics to avoid detection. They know thresholds, exploit product gaps, and move funds across jurisdictions to stay under the radar.
Rules alone miss subtle patterns. For example, a Council of Europe report detailed a case where an individual deposited €1m into two life insurance contracts, surrendered them shortly after, and transferred the funds abroad.
Individually, each action appeared legitimate, but collectively it formed a clear layering scheme that static rules failed to detect.
High false positives further strain compliance teams. UK insurers, including Allianz, experienced a rise in motor insurance fraud using doctored photos that rules failed to flag. Similarly, an insurance agent laundered over $1.5m through early policy surrenders, exploiting static systems that never adapted to emerging patterns.
The solution lies in AI-driven detection. Machine learning can identify risks based on behavioural combinations, adapt to new typologies, and prioritise alerts efficiently.
By integrating historical data, regional risk profiles, and entity linkages, insurers can build a dynamic view of risk, constantly evolving to catch previously unseen threats.
Compliance teams can immediately improve detection with five steps: audit existing rules, overlay AI, train models on real data, integrate cross-domain signals, and ensure monitoring evolves over time.
Rules are necessary, forming the foundation of AML programmes, but alone they are reactive and limited. By combining rules with adaptive AI, insurers can cut false positives, uncover hidden threats, and satisfy regulators’ growing demand for effective compliance.
Read the full blog from SymphonyAI here.
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