Governments around the world have been shining their regulatory spotlights on money laundering. As digital transformation makes an impact across multiple industries, it is no surprise that technology is playing an increasing role in the anti-money laundering (AML) fight.
Artificial intelligence (AI) is a great technological tool for fighting financial crime, particularly money laundering, but it still has its limits necessitating human interventions and expertise. For example, algorithms may not always work seamlessly with real-world situations and AI applications are only as good as the data provided. That said, AI has the potential to help bring down costs for companies and boost growth, which makes it a very attractive investment. The time is now for making that investment as a powerful tool in the AML toolkit.
Getting regulators on board
AML regulations are broadly similar across jurisdictions, but there are certainly differences in where different regulators stand on the use of AI and other technologies to combat such crimes. Some countries, such as Singapore, are advocating increased use of technology, albeit cautiously, while others are interested in its potential and are still catching up.
To be able to take AI forward and satisfy all stakeholders, governance and human oversight is essential - the technology cannot exist on its own in my view. Proper frameworks are required to govern the use of AI and other technologies so that they are not simply used as a cost-cutting measure. Viewing the digital transformation of AML functions through the narrow lens of saving money diminishes its effectiveness in the real world. Domain knowledge is needed for the technology to make a true difference.
Making the bold transformation to AI
An exciting future awaits - if the application of AI is done right. Companies should focus on building an analytics and AML team that cross-pollinates everyone's skills, making the most of everyone's strengths as well as leveraging the technology properly. The right combination of technologies and domain knowledge utilizing the best of machines and humans, has the potential to be world-beating and good for regulators.
A common pitfall is to leave the machine-learning model development to the data scientists without involving the AML specialists, who can feed their insights into a model to create a bank of knowledge that can improve the capacity of the AI technology to help detect possible criminal activity - as well as making compliance teams more effective at their jobs.
While AI technology for AML applications is at its most powerful when data is managed properly, it is unreasonable to expect data to be perfect. Ideally data needs to be tracked, gathered and analyzed meticulously at every stage of the value chain. This is the best way to help ensure any suspicious activity can be picked up in a timely manner and quickly and accurately analyzed. As a result swift, competent decisions can be made about any action that needs to be taken.
A bright future awaits
The good news is that there is evidence of a better understanding among companies and regulators of the power and potential of embracing AI to fight money-laundering. There has been some understandable skepticism surrounding the use of AI for AML purposes, as is the case with most new technologies - history shows that everything from the motor car to the internet was met with doubters at the beginning - but this will likely ease as its expected benefits become clear to more organizations and regulators. When it is clear that scalable AI solutions play a vital role in robust compliance, I believe acceptance of the technology should become more widespread.
An exciting future where the gap is bridged between traditional means of fighting financial crime and leveraging state-of-the-art AI technology to its full potential is close. Rather than companies and regulators playing catch-up with late investment in AI for AML functions, now is the time to seize the digital day.