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11.02.2026|

Banks aren’t the only target for money launderers – they also aim to channel illicit funds into the financial system via insurers. Their goal? To transform illegal assets into seemingly legitimate payouts.
Life insurance policies with investment components, single-premium annuities, or products with surrender values and early cancellation options are particularly attractive.
Investigations show that insurers run into regulatory trouble when AML safeguards are lacking. Supervisory authorities have imposed multimillion-euro fines for failures such as:
Insurers submit significantly fewer SARs than banks, as the 2024 annual reports of Germany’s FIU and Switzerland’s MROS confirmed. However, the same legal requirements apply, including KYC/CDD processes to identify contracting parties, determine beneficial owners, and screen against sanctions and PEP lists.
And there’s more. Detecting potential money laundering in new business, existing policies, and claims handling is a dynamic process. Insurers have no choice but to update their methods on an ongoing basis to uncover anomalies.
Detect money laundering
Software for anti-money laundering and customer screening helps insurance companies detect anomalies in insurance policies, match personal data against sanctions and PEP lists, and monitor transactions.
To detect money laundering or terrorism financing, insurers commonly apply a risk-based approach as part of an industry standard that the FATF also recommend. The risk profiles involved, however, differ across new business, in-force business, and claims.
When taking on new business, insurers verify the applicant’s identity, check sanctions and PEP lists, and assess creditworthiness. Other patterns they tend to flag include:
Behavioural changes in policyholders can be a red flag, including:
When processing surrender or partial withdrawal requests, insurers watch for signs like:
For insurers to meet due diligence obligations, they have to routinely compare their data against sanctions and PEP lists, as well as internal watchlists. But the enormous data volumes involved make the challenge equally huge.
It’s not just about customer data, but also contractual partners such as claimants or beneficiaries—against millions of records on official lists.
To process such volumes and identify matches reliably, a high-performance screening solution is crucial. Unfortunately, not all matches are genuine and many are false positives. And technically, despite allowing for such matches, AML officers still have to manually check them off. Any hit that proves a non-match wastes valuable time and limits any capacity to assess real threats.
A tailored approach can mitigate this. Configuring the system to the insurer’s risk profile and applying pre-filters to exclude irrelevant entries is key to streamlining the whole process.
In Germany, insurers subject to the Insurance Supervision Act (VAG) must establish a dedicated compliance function. BaFin serves as the competent supervisory authority and sets out its expectations in the Interpretation and Application Notes (AuA).
In Switzerland, insurers may choose between direct supervision by FINMA or adherence to the self-regulatory organisation of the Swiss Insurance Association (SRO-SVV). In Liechtenstein, the Financial Market Authority (FMA) provides regulatory oversight.
The EU Anti-Money Laundering Package, coming into force in 2027, will introduce new obligations. This comprehensive framework comprises three key legislative acts:
Additional guidance comes from the FATF, including Recommendation No. 10 on customer due diligence for insurers 10D.
Insurers are increasingly implementing measures to strengthen their AML capabilities. Whilst regulations don’t explicitly require transaction monitoring systems, the sheer volume of customer, partner, and account data, combined with sanctions list entries, can only be managed effectively through modern technology.
Three clear trends are emerging:
The shift towards cloud-based operations is accelerating. Many insurers are actively considering AML system migration to the cloud, motivated by:
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