The case for artificial intelligence in combating money laundering and terrorist financing
Machine learning technology
Combating money laundering is an enormous task, and it comes with substantial costs and risks, including but not limited to regulatory, reputational and financial crime risks.
Managing these risks rest with the guardians of the financial system. Moreover, criminals continue to evolve in their laundering techniques, finding and exploiting loopholes in the system to move money. These criminal minds are also capable of using new technologies such as online banking, electronic payments, and cryptocurrencies to move illicit funds across borders at breakneck speed. This creates complex and layered transactions that are increasingly real-time, making it difficult to monitor and to detect with traditional approaches.
At the heart of criminal activity are sophisticated money launderers with the ability to move illicit funds seamlessly through the formal financial system. These money launderers are sophisticated and pose a serious threat to financial institutions across the globe, and their activities have a devastating consequence for society as well. As a result, societal ills such as terrorism, drug and human trafficking challenge social structures and order, societal governance, as well as open and fair commerce. For these reasons, the importance of continuous improvement of an organisation’s financial transaction monitoring and name screening effectiveness has never been more critical in the digital age.
Download the joint whitepaper by Deloitte and United Overseas Bank (UOB)entitled “The case for artificial intelligence in combating money laundering and terrorist financing” that discusses the promise of machine learning in compliance and its potential applications. The whitepaper also highlights a case study that depicts UOB’s journey to tap machine learning to augment and to enhance its existing systems to spot and to prevent illicit money flows.