AI is being employed in the battle against money laundering and other financial crimes. Duncan Taylor, Infinity’s Head of Compliance, takes a look at the role machine learning can play in meeting AML and KYC regulatory requirements.
The fight against money laundering and other financial crime is a never-ending battle. No sooner have financial institutions got to grips with one crime, then the criminals have moved on to new methods and alternative strategies are required.
The rise of the digital economy during the Covid pandemic has opened up new avenues for financial crime and the perpetrators have been quick to exploit them. Often financial institutions are hampered by outdated legacy systems and software which make it hard to comply with updates and new measures which are introduced to reinforce AML frameworks. The criminals, by contrast, are nimble and can pivot their methods fast.
There is strong regulatory pressure for companies to deal with the problem of financial crime. The penalties for anti-money laundering (AML) and Know Your Customer (KYC) failings have risen around the globe. In APAC (Asia Pacific Countries) this has led to fines of over $US5.1billion for financial institutions in the region for non-compliance with regulations. That compares to US$6.6million in 2019 – a hefty increase! APAC has been particularly hard hit compared to other regions because of inconsistencies across the region in adopting and implementing AML measures.
Companies are having to invest in increasingly sophisticated technological tools in the incessant struggle to win this game of cat and mouse. AI, and specifically machine learning, is proving to be key in strengthening compliance. Machines are being taught to flag up suspicious transactions which merit further investigation and can obviously process huge volumes of data far more quickly than a human being can. Ideally, this should be done at the onboarding stage to catch suspicious cases early and ensure that true compliance risks can be fully investigated by compliance teams within the organisation and referred to regulators if warranted. Nevertheless, the behaviours of onboarded clients must be continually monitored for AML risk.
In spite of its potential, business leaders in many financial institutions are struggling to get to grips with AI and how it can be implemented within their organisations. There is often little clarity on what it does, how it can be used and how it will affect customers, along with issues of cost and integration with existing systems. Often the first step is to streamline data management.
One thing is certain: financial institutions are going to need better processes and smarter tools to swiftly identify money laundering activities in the face of constantly evolving AML and KYC regulations. While costly to implement , integrating AI and machine learning into their operations will ensure that companies avoid incurring huge penalties which not only damage their bottom line but also their reputation.
I have over 20 years of experience in the financial services industry and hold a Chartered FCSI qualification. I ensure that our operations are fully compliant with the rules of our most stringent regulators.