Exploring the Use of Technology in AML
The anti-financial-crime landscape continuously evolves as technology solutions designed to facilitate financial crime detection become ever more sophisticated. Advancements in machine learning have enabled firms to implement new processes and technologies that help compliance officers and anti-money-laundering (AML) investigators around the world investigate illicit activities in the financial ecosystem. But how can the financial industry be incentivized to continue the adoption and implementation of new solutions? And how can new machine learning-based solutions help the fight against global financial crime?
These were some of the topics discussed by a panel at the Brussels-based FinTech & Regulation Conference. Nasdaq’s Darren Innes joined other panelists from the FinTech space as well as regulators and law enforcement agencies at this year’s virtual conference.
A global problem that requires global collaboration
Financial crime is a complex area not confined by geographical borders, making it even more important for stakeholders to collaborate across organizations and country borders. Constructive dialogues and shared feedback between the private sector and the public sector are parameters that are equally as important in order to create synergies and learning opportunities in the fight against financial crime.
“A key part in fighting financial crime is to improve information sharing,” Innes stated during the discussion. “We all understand that we are better and stronger as a whole rather than as individuals. By piecing together the many different pieces of the puzzle that we all have individually, we get a better holistic picture. Ultimately, the private sector and public sector working alongside each other will create better conditions to fight financial crime.”
Many AML professionals encourage collaboration and information sharing between financial institutions and law enforcement. Part of the process in improving collaboration is to guide people and institutions to become more comfortable and proficient in using emerging technologies. In recent years, many advances have been made in the data sharing space. Innes explained that “federated learning is one type of machine learning technology that has emerged. Federated learning is not about sharing personal data, but it’s rather about sharing data models and insights on what we learn from a breadth of data. That will enable us to build best practices across the field since it allows us as to, as a collective, build a common machine learning model without actually sharing the data across organizations.”
The other way to share information is through homomorphic encryption, Innes stated. Homomorphic encryption allows computation on encrypted data, essentially letting data remain anonymous while it is processed and analyzed. This method allows organizations that share their data to retain control over which individuals get to see the data.
Machine and human collaboration for effective impact
While technology becomes more sophisticated, there is concern that technology can make the human compliance officer obsolete, a concern shared by many industries facing technology disruption. Innes elaborated on this: “The future of the compliance officer is not compromised. Technology is not there to replace the human. At Nasdaq, we see technology as a complement to the human compliance officers. Technology is there to enhance the work of compliance and mostly to give compliance teams more time to spend on investigating crimes rather than manually sifting through massive amounts of data. We need to remember that a majority of transactions are not bad. New machine learning-based tools are there to help compliance officers focus their time and make precise choices. Similar to many other professions, the work of an AML or compliance officer is not static. So with new technologies augmenting the toolbox, perhaps a different skill-set is needed. But again, technology is there to help and to alleviate.”
While there’s a growing number of AML technology solutions available, the panelists noted that knowledge in how and where to implement technology is equally as important for impactful outcomes. Innes explained: “As technologists, we should remember to encourage others such as regulators to adopt new technology. We need encouragement and guidance in order to remove barriers to technology implementation. Only when technology is implemented in the right place can it become efficient enough to help people focus on areas where they are most needed.”
“New machine learning-based technologies have huge potential in the fight against financial crime. However, when focusing on using the right technology to solve the right problem, that’s when we will see wide adoption and when technology will be truly impactful,” Innes concluded.
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