The insurance sector has traditionally received less attention than banking when it comes to financial crime compliance, yet it represents a significant vulnerability for money laundering. Research from the Napier AI / AML Index suggests that as much as $3.3tn could be returned to global economies through the adoption of AI-driven anti-money laundering strategies.
Within this broader risk landscape, the Financial Action Task Force (FATF) has repeatedly highlighted life insurance and investment-linked products as particularly exposed.
Their structure allows illicit funds to be absorbed, transferred and ultimately disguised through what appears to be legitimate policy activity, making them attractive vehicles for criminals.
This risk is not theoretical. Regulators across multiple jurisdictions have already recovered tens of millions of dollars in proceeds linked to drug trafficking that were laundered via insurance products. Common techniques include surrendering policies early, deliberately overpaying premiums to trigger refunds, and transferring policy ownership between connected parties. These activities can be difficult to spot in isolation, especially when they mimic normal customer behaviour, but collectively they point to systemic weaknesses in insurance AML controls.
How to meet these challenges head on
To address these challenges, insurers are under growing pressure to enhance their AML frameworks through a combination of technology, tailored detection rules and robust reporting. A clearer understanding of red flags is a crucial starting point.
Criminals frequently exploit early surrender clauses, accepting penalties in exchange for apparently “clean” funds. Others make use of cooling-off periods to cancel policies and receive refunds, or reassign ownership to family members and associates who then borrow against the policy value.
The use of multiple small policies instead of a single large one, premium top-ups after an initial low-value purchase, secondary market sales of life policies and third-party premium payments are also widely recognised typologies. What unites these methods is their ability to obscure the origin of funds while operating within legitimate insurance mechanisms.
One of the most effective ways to improve detection without harming the customer experience is through the use of sandbox environments and artificial intelligence. Traditional, blanket rules often generate high volumes of false positives, placing strain on compliance teams and creating friction for customers.
A sandbox allows compliance officers to test and refine AML scenarios in a controlled setting using historical or synthetic data. Insurers can model different risk profiles across products, fine-tune thresholds for behaviours such as frequent early cancellations, and trial new typologies linked to high-risk geographies, all without disrupting live systems.
Once these rules are validated, AI can be deployed to analyse large datasets, distinguish genuine threats from noise, and significantly reduce unnecessary alerts.
Demonstrating efficiency
Detection alone, however, is not enough. Insurers must also demonstrate to regulators that their controls are effective and proportionate. Recent Financial Conduct Authority consultation CP25/12 signals a shift towards more risk-based reporting, replacing routine annual notifications with targeted reporting of significant breaches. This places greater emphasis on transparency, data traceability and the ability to evidence compliance on demand.
Well-designed reporting frameworks provide clear audit trails of rule changes, testing and outcomes, helping to build regulatory confidence while also supporting faster, smoother customer journeys.
With money laundering estimated to cost the global economy $5.5tn each year, insurers can no longer afford to sit on the sidelines. Leveraging AI, sandbox testing and meaningful reporting is fast becoming essential to managing AML risk in an increasingly complex insurance landscape.
Read the full blog from Napier AI here.
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