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The insurance industry is often overlooked when it comes to financial crime compliance, but it’s a prime target for money launderers. According to the Napier AI / AML Index, $3.3 trillion (USD) could be returned to economies worldwide with AI-powered AML strategies. Within that global exposure, the Financial Action Task Force (FATF) consistently identifies life insurance and investment-linked insurance products as higher-risk channels for money laundering, due to their ability to absorb, move and disguise illicit funds through legitimate policy activity.
Regulators are recovering tens of millions in drug money funnelled through insurance products using tactics like early policy surrender, premium overpayments, and ownership transfers.
To counter these threats, insurers need to strengthen their AML screening with technology, tailored rules, and meaningful reporting.
Common red flags in insurance AML
Criminals use a range of tactics to exploit insurance products in subtle ways as they provide legitimate ways to move and access funds. Some of the most common include:
- Early surrender: Policies are purchased with illicit funds and cashed out early. Even after penalties, the remaining funds appear legitimate.
- Overpayment and refunds: Criminals deliberately overpay premiums or cancel during a ‘cooling-off’ period to receive reimbursements that look like clean money.
- Ownership transfers: Policies can be reassigned to family members or associates who then access or borrow against the funds.
- Secondary market sales: Life insurance policies can be sold to third parties, creating complex layers and networks of transactions.
- Multiple small policies: Instead of one large policy that draws scrutiny, several smaller policies are purchased to avoid detection.
- Top-ups: A low-value policy is bought first, then significantly increased with additional payments.
- Third-party payments: Premiums or refunds are funded or redirected to individuals not named on the policy.
These tactics share a common trait by attempting to mask the origin of funds while exploiting legitimate insurance mechanisms.
Using sandboxes and artificial intelligence for operational efficiency
One of the biggest challenges in insurance AML is balancing effective detection with a smooth customer experience. Blanket rules can generate unnecessary alerts, frustrate customers, and drain compliance resources.
A sandbox environment provides compliance officers with a controlled and isolated space for testing, tuning, and running ‘what-if’ scenarios using live or sample data. This allows insurers to safely experiment with thresholds and scenarios such as frequent early surrenders or sudden premium top-ups, without making disruptive changes in production systems.
By testing scenarios safely with historical or synthetic data, insurers can:
- Model specific risk profiles for different products, such as life insurance vs. short-term travel policies.
- Fine-tune thresholds for unusual activity, like premium top-ups above a certain percentage or frequent early cancellations.
- Test new typologies without disrupting live operations. For example, experimenting with rules that flag third-party premium payments linked to high-risk geographies.
- Measure the impact of rules before full deployment, ensuring they catch true risks while minimising false positives.
This approach allows insurers to apply a risk-based framework in line with regulatory expectations, focusing on the highest-risk products and customer behaviours.
Once AML rules are defined and tested in a controlled sandbox environment, artificial intelligence can take over to cut through the noise in screening processes. By analysing large volumes of transaction and client data, AI can distinguish between true risks and false positives with far greater accuracy than manual or purely rule-based approaches. This enables compliance teams to focus on genuinely suspicious activity while minimising unnecessary alerts, improving both efficiency and the customer experience.
Customised reporting for actionable Insights
AML screening in insurance doesn’t end with detection. Insurers must demonstrate to regulators that their systems are not only in place but working effectively. Regulatory compliance reporting is a critical part of this process.
Recent Financial Conduct Authority (FCA) proposal (CP25/12) highlights a move towards proportionate, risk-based reporting. The consultation proposed to remove annual notifications to the FCA, replaced with requirements to notify regulators only of significant breaches of rules, aligning with broader supervisory practices. Firms must maintain transparent records and be prepared to evidence compliance on request, particularly around data traceability and customer outcomes.
For insurers, this means compliance reporting should not be seen as a tick-box exercise but as a tool to build regulator confidence. Effective AML reporting frameworks should provide clear audit trails of rule changes, testing, and outcomes. For customers, this translates into a smoother, faster experience. Policies are issued and managed without unnecessary delays, while firms still maintain robust controls against financial crime.
Money laundering is estimated to cost the global economy $5.5 trillion (USD) each year.
Explore the Napier AI AML Index to see how financial crime risk varies by country, sector and regulatory effectiveness – and what this means for insurance AML exposure.
