How can technology help insurers with the fight against money laundering?


Money laundering is a growing threat within the insurance industry. The regulatory framework within banking is adding stronger controls and governance processes which will encourage launderers to seek alternative areas to launder funds.

While insurance presents a different type of Anti-Money Laundering (AML) risk, the risks still exist. Long considered a low priority for regulators, opportunities for money laundering and terrorist financing are now on the rise.

Focus on changes

The very nature of insurance products, with a term approach to some policies, can present a lower risk from a monetary transactional perspective. For example, an insurance policy may consist of a single payment to cover a duration of time (e.g. hours, days, weeks or even years). Even so, firms need to focus on changes. For example, mid-term changes in policy details, like adding a party or fundamental alterations requiring a partial refund or even paying a top-up into a life policy. These are all transactions and changes to the relationship that could or should be considered flags for consideration.

It is even necessary to consider any cooling off periods associated with policies – the ability to identify monetary income as that from a cancelled policy presents a route for money launders to use. Firms need to stay alert and understand how material the changes are; does this mean an event-driven review should be considered to re-evaluate for AML risk? Therefore, insurance firms need to understand the nature of the products and opportunities for money laundering – and the associated risks.

Some insurance firms have implemented compliance programmes relying on sanctions screening, red flag and rule-based threshold detection methods. Insurers also enhance these approaches by providing sufficient staff training and the tools to identify suspicious activities and investigate issues. The approach to detection needs to be considered carefully. Some approaches to AML detection can result in as high as 95% false positives; all of which require due-diligence to be completed on. This is typically a manual review process – with associated high costs.

AI and machine learning for AML

Technology is not always the answer – but in complex environments, such as insurance, appropriate usage can better target and identify unusual activities and help mitigate risk from a money laundering perspective. Additionally, COVID-19 accelerated insurers' focus on using advanced analytics technologies to detect financial crimes, such as Artificial Intelligence and Machine Learning, which can enhance existing systems and better target resources within firms when implemented correctly.

Insurers can use analytics to take a risk-based approach for customer / policy monitoring - effectively automating the risk scoring associated with a combination of policy, transaction and customer. This would allow for consideration of monetary and non-monetary transactions, to comply with AML regulations. While not all firms are subject to complete AML regulations in the UK, 5MLD (the 5th Anti-Money Laundering Directive) brought a greater alignment with global regulations. And the Proceeds of Crime Act 2002 still requires firms to implement adequate measures against financial crime.

Firms can benefit from an analytics platform and associated industry expertise, that can sit alongside their existing investments in AML compliance systems and processes. Adopting a more balanced approach to people and technology, enables each to play to their strengths. Automate where it makes sense but allow your team to focus on areas where domain and business knowledge is imperative to mitigate AML risk.

Find out more in this report: Acceleration through adversity- The state of AI and machine learning adoption in AML compliance.


About Author

Colin Bristow

Director, Pre-Sales Support

A wide range of experience gained over 15 years working with the financial services sector, Colin has spent time advising organisations in usage of machine learning, intelligent systems technologies and analytics for improvement in business advantage. Colin has worked globally in the areas of Risk, Fraud, AML and technology (including Hadoop) for a wide range of financial services firms. He has spent over 5 years working at SAS, and is currently responsible for supporting one of the global banking organisations in setting a strategy and direction for usage of advanced analytics.

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