AML Consortium Approach: Disrupting Financial Criminals

Adam McLaughlin, Global Head of Financial Crime Strategy & Marketing, AML

The financial crime compliance revolution is here, and it will change how we identify and disrupt financial criminals.

Financial crime is an ever moving target, and unfortunately criminals do not stand still. They constantly find new and intuitive ways to operate, including moving and hiding their illicit wealth. To make it even harder for crime fighters, criminals are not restricted by jurisdictional borders, regulations or laws. They work in consortium with each other to successfully operate their illicit business, whether that be making or moving illicit goods or moving and cleaning the wealth generated from their business.

Benefits of an AML Consortium Approach

The regulated sector does not have the same luxury of operating where they want or how they want. Regulated organisations need to conform to laws and regulations and comply with the restrictions of operating or sharing information over different jurisdictional borders. This means that the regulated sector has worked in silos, not sharing vital information with each other, which if shared could assist in fighting financial crime.

Industry and public agencies appear to be aware of the challenge that the inability to share information and work together has on effectively stopping illicit movements of wealth. Over the years, legislation has been introduced to facilitate information sharing between private sector organisations. In the UK, the Criminal Finances Act 2017 (Chapter 2, Section 11), in the EU the 4th Money Laundering Directive (Section 2, Article 39) and in the U.S., the US PATRIOT Act (Section 214(b)). The legislation in all of these cases has one thing in common: they are all voluntary. As a result, they are not utilised as often as they should, hampering the fight against the criminals.

The private sector is increasingly adopting an intelligence-led approach to financial crime compliance, which includes working together. Nordic financial institutions are leading the way in taking a collective approach to financial crime compliance. In July 2019, six Nordic banks set up a joint venture company to develop a platform for handling KYC data. The purpose is to consolidate KYC data, to standardise and simplify KYC processes, improve customer experience and strengthen financial crime controls with a unified record of customer information.

Working Together

The latest development is the Netherlands Transaction Monitoring utility. For five of the largest banks in the Netherlands, 97 percent of all accounts have entered into a consortium. They are working together with government partners to consolidate their transaction data and run a unified transaction monitoring solution. The aim is to enhance detection of financial crime by identifying suspicious transaction patterns between financial institutions and identify criminal networks.

There is one consistency between all of the current trends in a consortium approach: technology. A consortium approach to AML would not work without a technology solution utilising new advances in artificial intelligence (AI). Traditional AML systems were designed for a siloed world of AML compliance, while consortium monitoring and detection involves more data and complex patterns of behaviour across multiple financial institutions. AI helps to quickly understand patterns in network activity, assess what is normal versus abnormal and alert on abnormal activity. Adding machine learning to this ensures detection is constantly enhanced, learning changes to behaviour of criminal networks.

Technology is also available to allow financial institutions who are not part of a utility to benefit from the sharing of information, to enhance their financial crime detection. Consortium analytics utilises non-PII data from all financial institutions signed up to the analytics service to learn and optimise existing detection models, but also identify new typologies. Federated Learning is one such analytics technology. Federated Learning trains machine learning models based on multiple datasets from across several financial institutions, improving monitoring and detection. Shared data can also be used to identify seasonal variations in customer activity, both normal and suspicious, further optimizing detection.

The revolution in fighting financial crime has started. We have the tools that allow us to work as a collective and not in isolation. We can monitor and detect new typologies almost as quickly as the criminals can develop them. We are at the start of this transition and the next 12 months will be an exciting time. We need to work together to make it a success, along with clear guidance on existing legislation and introduction of new legislation. We all need to align on the outcomes we want to achieve and embrace new technology that will help make this revolution a mainstream reality.

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