AML explained for insurers: red flags, AI and reporting
The banking side gets most of the attention, but insurance carries real exposure to money laundering. Research indicates AI-driven AML could return trillions to the economy, yet many insurance programs still rely on broad rules and manual reviews.
FATF has flagged life insurance and investment-linked products as high-risk. Their structure can absorb and move funds under the cover of legitimate policy activity, which is exactly why criminals use them.
Why insurance is attractive for laundering
This risk isn't theoretical. Regulators across multiple jurisdictions have recovered tens of millions tied to drug trafficking that flowed through policies.
Products with cash value, surrender features, and flexible funding make it easy to insert, transfer, and withdraw money while looking compliant on the surface.
Red flags to operationalize now
- Early surrenders (even with penalties) that convert "dirty" premiums into apparently clean payouts.
- Cooling-off cancellations used to trigger refunds shortly after inception.
- Reassigning ownership to family or associates, followed by loans against policy value.
- Multiple small policies instead of one large policy to avoid scrutiny.
- Premium top-ups after an initial low-value policy purchase.
- Secondary market sales of life policies (life settlements) without clear economic rationale.
- Third-party premium payments inconsistent with the customer's profile.
- Deliberate overpayments to manufacture refunds.
Make these actionable. Set thresholds and time windows (e.g., frequency of surrenders in 90-180 days, refund ratios, ownership changes within a year), tie them to customer risk scores, and monitor patterns across related parties and channels.
Use sandbox + AI to cut false positives
Blanket rules clog investigations and irritate good customers. A sandbox lets you test scenarios with historical or synthetic data, tune thresholds, and validate new typologies without touching production.
Model risks by product and geography. Calibrate triggers for early cancellations, ownership transfers, premium anomalies, and third-party funding. When rules hold up, deploy AI to analyze large datasets, separate signal from noise, and reduce alert volume without missing true risk.
Reporting that stands up to scrutiny
Detection is half the job. You also need to prove your controls work and scale appropriately.
Recent FCA consultation CP25/12 points to more risk-based reporting and targeted notifications of significant breaches. That raises the bar on transparency, data lineage, and on-demand evidence of control effectiveness.
- Maintain an audit trail of rule changes, sandbox tests, sign-offs, and outcomes.
- Track alert quality (precision/recall), false-positive rates, investigation SLAs, and escalation decisions.
- Define "significant breach" criteria and document materiality thresholds.
- Record data lineage for every alert: source systems, transformations, and who touched what, when.
- Document AI governance: features used, performance monitoring, drift checks, and periodic revalidation.
A practical rollout plan
- Map product risk: prioritize life and investment-linked lines and any products with cash value or flexible funding.
- Stand up a sandbox: load historical/synthetic data, replicate top typologies, and tune thresholds to reduce noise.
- Layer AI after rules stabilize: use models to score entities and networks, then back-test for lift and fairness.
- Tighten processes: playbooks for alerts, breach criteria, and reporting packs that regulators can audit.
- Train teams: investigators, underwriters, and distribution on red flags and escalation paths.
- Schedule quarterly reviews: rule performance, model drift, typology updates, and effectiveness metrics.
Why this matters now
Money laundering is estimated to cost the global economy $5.5tn each year. Insurers can't sit out. AI, sandbox testing, and clear reporting create better detection, fewer false positives, and cleaner customer journeys.
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