To prevent money laundering in the insurance industry, analyzing insurance policies and customer data for any unusual activity is a must. AI-powered software can help.
Insurance Companies Strive for Increased Efficiency in Money Laundering Prevention
Insurance Policies Can Be Lucrative for Money Launderers
Money launderers target more than just banks when striving to bring illegal assets into the financial system. Insurance companies are also at risk and tend to be obligated to prevent money laundering when offering life insurance or accident insurance with premium refunds or when engaging in banking operations. This forces them to analyse their processes precisely to identify circumstances where money laundering prevails or potential links to terrorism financing.
Insurance companies submit fewer suspicious activity reports (SARs) to reporting agencies compared to banks, as the latest annual reports from the German Financial Intelligence Unit (FIU) and the Swiss MROS exemplify. However, they remain subject to the same legal requirements, including the KYC/CDD process for identifying contract partners, determining the beneficial owner and checking against sanctions and PEP lists. But that’s not all. Detecting money laundering-related issues is an evolving process in constant flux. Insurance producers must stay on top of it to uncover unusual activities, and ensure compliance with legal requirements, prevent reputational damage and penalties, reduce false-positive alerts and minimise manual processes.
Money laundering Indicators
Most insurance companies apply a risk-based approach to detect money laundering or terrorist financing. However, the risks differ.
New customer business includes identification, sanction list and PEP check of applicants, plus a credit check. Insurance producers also often conduct checks when any of the following apply:
- Conclusion of several capital-forming contracts within a short period
- Unusually high additional payment when the contract is concluded
- Transfer of residence abroad shortly after concluding the contract
- Replacing a contract with a low regular premium payment by a contract with a high single premium payment without taking downsides into consideration, e.g., taxation or interest loss
- Priority interest of the policyholder in early termination or surrender values
- Desired payment of the single premium in cash or in foreign currency
Money Laundering Risks in Existing Business
Behavioural changes from a client or customer could be a red flag that raises the risk of money laundering. Let’s examine some scenarios that may warrant further investigation more closely:
- Additional payment for existing contracts, especially if made by a third party
- Unclear funding source when loans are repaid
- Frequent change of subscription rights
- Premium payment from abroad
- Change of policyholder
Money Laundering Risk in the Event of a Claim
Applications for buyback or partial withdrawal, for example, will trigger checks on the part of the insurer to ascertain whether there were any frequent early cancellations or partial terminations in the first years of insurance or if the pay-out is to be made to risk-prone banks.
Why Insurers Expect Demand for Compliance Personnel to Grow
A study, by Versicherungsforen Leipzig (Germany) and the law firm BRP Renaud und Partner mbB, revealed decentralised compliance was the norm for almost three quarters of insurance companies. Compliance officers have been appointed in specialist departments alongside an additional central compliance function. But over three quarters of all respondents see the need for staff in the compliance sector growing in future. In response, insurance companies are trying to use technologies that work automatically and check unusual processes, movements and transactions, even if they fall outside the standard pattern.
Drastic Shortage of Skilled Professionals
Relieve Compliance Officers through Machine Learning
Many compliance processes are recurring and rule-based to detect terrorist financing, money laundering, market abuse, and insider trading. Machine learning complements these rule-based tasks, enabling compliance officers to prioritize the high probability True Positive hits.
The Challenge of Sanction and PEP List Checking: Excessive Data Volume
The task facing insurers is to regularly cross-check their data against sanctions lists, PEP lists and other blacklists. That’s all very well – except they are drowning in excessive data. Over and above the data of millions of customers, this also includes the data of contractual partners like claimants. There are also millions of data on the sanctions lists.
So, in response, the use of high-performance software to handle such data volumes is crucial. Once the software finds a match between the data of a person on a sanctions list (e.g., by name, country, birthdates, etc.) and the customer database, it will indicate a “hit”.
Don’t Waste Time: Reduce False-Positive Hits With Machine Learning
False positives are an issue that often blights anti-money laundering (AML) compliance. And while flagging them is a must, manually reviewing individual examples can eat up valuable time and resources. That said, what if you had a way to streamline the process and prioritise true positives only? That’s where machine learning comes in. Harnessing an optimised comparison algorithm lets it analyse and prioritise hits, ensuring that time and resources go only on cases truly worth investigating. For no more wasted time and a more effective AML compliance process, leverage the power of AI.
Checking new and existing customers for matches with entries in sanctions and PEP lists. Machine learning achieved soaring improvement over the previous checking algorithm: Up to 57% of the false positives having occurred to date pose no risk and the remainder are unlikely to do so.
Ensuring Regular Monitoring of New and Existing Customers
Periodically, insurance companies as required to check and update the identification data of their policyholders on an ongoing basis. This may be done even more regularly in the event of key changes in premiums or other circumstances that are relevant to money laundering. Accordingly, insurers monitor their contract partners before and during the contract term for scenarios such as:
- High additional payments when the contract first concluded
- Partial terminations when the business relationship commences
- Short-term repayments
- Change of beneficiaries
- Credit assessment inappropriate to customer behaviour
- No disclosure of account details at the time of disbursement
As soon as one of the abovementioned scenarios occurs, it is incumbent on the money laundering officer to perform the check and initiate further measures.
Anti-money Laundering in the Cloud: A Clear Trend
The shift towards SaaS applications is gathering momentum and this includes when processing sensitive (i.e., compliance-related) data. No wonder the idea of relocating anti money-laundering initiatives to the cloud appeals so much to insurance companies, for the following reasons:
- Resource scarcity for on-premise operations, precluding scope to safeguard operations.
- Scope to simplify expertise for first- and second-level support by a managed service, assisted by a software provider.
- Costs. We can assume that for most insurance companies, the costs of on-premise operations exceed those of a cloud solution.
Legislators and Financial Supervisors Demand Compliance Functionality
In Germany, insurers that fall within the scope of the Insurance Supervision Act have the obligation to establish a compliance body (Sec. 29, § 1 Insurance Supervision Act (VAG)). BaFin acts as the supervisory authority. In its Interpretation and Application Instructions (AUA) of October 2021, it documented that the AUA applies to all obligated parties under the Money Laundering Act (GwG) that are under BaFin supervision pursuant to Section 50 No. 1 GwG. This also includes insurance companies.
In Switzerland, insurance companies can choose to be supervised by FINMA or join the self-regulatory organisation of the Swiss Insurance Association (SRO-SIA). In the Principality of Liechtenstein, the FMA acts as the supervisory authority.
For the life insurance sector, the Financial Action Task Force (FATF) issued the Guidance for a risk-based approach for the life insurance sector. In it, the risk of money laundering and terrorist financing is classified as lower than for other financial products (e.g., loans) or other sectors (banks). Even so, the guidance suggests that concluding a life insurance policy may be connected to criminal predicate offences.
The core benchmark for FATF when it comes to implementing the 40 FATF recommendations is the risk-based approach (RBA). The RBA means that financial supervisors and supervised entities identify and understand the money laundering risks to which they are exposed and take the appropriate action.
Insurance producers are redoubling efforts to ensure their money laundering prevention remains on point. The volume of data, comprising customer and partner data, account data and sanctions list entries, is practically only manageable with good software. This also includes the topics of automation and cloud application. Three trends stand out:
- Insurance producers define which money laundering scenarios exist for new and existing customers with increasingly high precision to maximise the detection rate.
- Machine learning makes sense as a second layer, as well as rule-based systems.
- Relocating the application to the cloud, even with the sensitive issue of compliance and operation of a SaaS solution.
Our experts look forward to hearing from you to look at your situation as an insurance company and leverage best practices from our projects.
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