Articles - Combatting the rise of money laundering in insurance

Financial criminals are increasingly using insurance products to clean dirty money and commit illicit crimes . The industry might not have the obvious routes for laundering cash that banks present, but there are still many opportunities. Furthermore, insurers are seemingly an easier target because fewer counter measures are in place. This needs to change – fast. Insurance companies must find and report these criminals to protect their reputations and comply with regulations.

 By Saeed Patel, Group Director at Eastnets

 The UK government updated money laundering regulations in 2020, reflecting similar legislation from the US Financial Action Task Force (FATF) and the EU’s 5th Money Laundering Directive . The rules highlight insurance policies as high-risk factors.

 How do criminals capitalise on insurance products?
 This begs the question, how are criminals using the insurance sector to legitimise their ill-gotten gains? There are number of ways, an obvious one being annuity policies. Once a lump sum has been paid, the launderer is entitled to a regular payment from the insurer. The same can be done with single premium policies. A one-off chunk of dirty cash goes in, and a neat bundle of fresh money comes out when a claim is made.

 Surrendering a policy can have the same effect. A life insurance policy can be bought in a single premium and then surrendered for a fee. This might be 10 per cent on average, but it’s a small price to pay for the cash that’s free to spend without worry of capture.

 If these methods still feel a little risky, there is always the opportunity to top up a policy. An initial, unremarkable premium might be paid to avoid suspicions. This can then be topped up with extra payments, which slowly build up before a surrender or claim.

 Furthermore, a criminal can always take out a loan against a life insurance policy. This will eventually be repaid at the end of the policy, hiding the cash in an intricate web of lies. There are also options to transfer policy ownership.

 Getting wise to the tricks
 It’s clear there are a wide range of techniques available to the ardent fraudster. Which means insurers need to know more than they may traditionally have asked of their policy holders to spot suspicious behaviour.

 The first step for any insurance company is to know who it is serving. It must have procedures in place to quickly seize and record data on new and existing insured members. Ideally, this will be saved in Know Your Customer (KYC) forms that can be easily customised to support internal onboarding requirements while also meeting local regulatory obligations.

 What’s more, this data needs to be screened, checking customers against sanction lists, politically exposed person (PEP) lists and adverse media lists. The best way to achieve this is through an automated system using distributed ledger technology (DLT), which is also the basis of Blockchain. This automatically draws upon lists from governments so the moment a person or entity is added to a database, the insurer has the latest data and can be fully compliant.

 With this information, it’s possible to assign dynamic risk scores to insured members, policies, and transactions. This can support compliance team members to prioritise alerts, make decisions, and decide the right level of due diligence for each insured member and their transactions.

 To help them in this aim, insurers might consider using AI-driven trend analysis and behaviour identification engines to collect and analyse data based on a mix of ready-made and customised scenarios for identifying money laundering typologies and suspicious activity.

 This might include spotting large top-up payments, large transaction amounts, excessive premiums, unusual payment methods, an unusual number of policies of the same type, early or frequent surrenders, change of ownership, policy loans, and cancellations. Essentially, it can watch for all the risk factors associated with money laundering via insurance.

 One drawback with this type of trend analysis is the creation of false positives. To reduce this, AI models can be used to optimise and calibrate alerts, significantly reducing noise while highlighting the policyholders, policies, and transactions that call for further investigation.

 Understanding and managing data
 Many businesses might consider that with these processes in place, they’re protected. However, it’s worth remembering that having access to data doesn’t equate to protection. Employees need to understand and manage it effectively to combat money laundering.

 It’s worth considering ways in which to correlate and visualise data, delivering high-value intelligence to staff in a user-friendly interface so they can easily manage risk. To explain this in a little more detail, it’s the difference between giving staff spreadsheets of inaccessible information to wade through, compared to receiving a map that shows them where the dangers are and advice on how to avoid them.

 In practice, automated systems that visualise data in a way users can digest easily are vital if the industry is going to tackle financial crime. What’s more, multiple users also need access to it to avoid intelligence sitting in silos.

 Finally, the entire process needs to be transparent with reports to prove compliance. The right information needs to be readily accessible to the right regulatory parties at the right time, simplifying and ensuring compliance with existing and emerging rules and regulations.

 All of this is vital. Because the more insurers know about the policy holders they serve, and the various complex schemes they may deploy, the better prepared they are to spot suspicious behaviour.

 Ultimately, this will protect the insurer, protect policyholders, and ensure the overall integrity of the insurance market. At a time when sanction lists have swelled, and the amount of money laundered stands at $2 trillion globally , it has never been more important.

 Insurance companies need to become as wise to the threat of money laundering as their banking cousins. Fast.



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