Managing money laundering risks in fast growing payment solutions

The payment solutions segment within fintech has grown exponentially. With faster, more flexible payment services, the payment chain linking multiple banks, tech providers and end-consumers becomes correspondingly complex. Money-laundering risk across complex payment chains contra owning the customer relationship and legal responsibility will warrant much more work in future.

Anti-money laundering is about preventing risks. Where the legal definitions are unclear and subject to interpretation, companies are best positioned to make sure that real risks are managed rather than to exclude them from the risk assessment scope. Out-scoping, which can be unintended due to lack of intelligence, lead to risks simply not being addressed.

Cover all parts of the business

The assessment of risk exposure needs to be ‘holitistic’ and cover all areas of the business, an involve appropriate expertise and data sources for comparison. Further, many organisations may benefit from ensuring clarity at the stage of their new product approval processes, to understand transaction flows and where risks can emerge. And finally, potential money laundering risks should be evaluated in relation to the business strategy and potential impact, both legally, financially and in terms of brand. 

Systematic approach across functions

Product and customer process AML will only be effective when organisations work systematically and holistically. A risk-based approach and processes that are aligned with the annual wheel, where information flows from risk assessment to the controller and back to SAR. What makes a difference between AML compliance by law and effective AML based on risk is the ability to operate across functions. As organisations have highly specialised functions, working on very specific issue areas, it is a common challenge to avoid silos and make sure AML is effectively integrated in the business. 

Taking measures

Continuous improvement in terms of effectively managing risks and non-conformities is the final part of the annual wheel. The ability to address identified weaknesses requires stamina, distinct measures and a mindset which safeguards internal openness.

Especially in segments of the financial services market with fast developing payment product offerings, risk assessment including the ability to classify customers according to risk category, routines and internal KYC procedures and customer monitoring will have to be correspondingly fast and well-developed. A fundamental step is to understand according to the ML/TF exposure to end users or customers. With a position at the intersection between banks and for example gambling companies, often regarded as especially exposed to money-laundering, payment solutions providers can see transaction flows in a way that other actors in the payment chain cannot. Given the exposure and the position of having more intelligence at hand comes a greater potential to mitigate a heightened risk.

Need for speed vs cash

While payment solution providers win markets with even faster and more flexible product offerings, including prepaid and gift cards, cash is a problem. Cash is still extensively used, even in digitalised economies. While we are all familiar with the term ‘cash is king’, we are also seeing that frequently ‘cash is criminal’. Organised crime is notably associated with extensive use of cash and are misusing new products and the challenges of complex money trail. 

FCG helps clients establish good procedures and measures across all areas of AML, including real-time monitoring of money-laundering risk, model validation and with particular regard to the above discussed issues transaction frequency, high risks linked to swish- and international payments and machine learning applications to prioritise valuable alerts. FCG is an independent advisor with regards to system vendors. 

Louise Brown


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Managing money laundering risks in fast growing payment solutions Managing money laundering risks in fast growing payment solutions
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