Why banks should embrace Artificial Intelligence & Machine Learning for their AML Compliance – Quickly and Now
Authored by: Sekhar Katkam - Banking and Financial Services Executive
The Bank Secrecy Act requires financial institutions in the US to assist US government agencies to detect and prevent money laundering.
The Bank Secrecy Act also known as the Currency and Foreign Transactions Reporting Act requires financial institutions in the US to assist US government agencies to detect and prevent money laundering. In this regard, the act requires financial institutions to keep records of cash purchases of negotiable instruments, and file reports of cash purchases of these negotiable instruments of more than $10,000 (daily aggregate amount), and to report suspicious activity that might signify money laundering, tax evasion, or other criminal activities. Banks also look for evidence of structuring – smaller transactions which are made in multiple numbers to avoid reporting.
The process of detecting money laundering has become more and more complex due to disparate transaction data systems, integration issues with monitoring systems and analysis involved for the multitude of transactions and tracking of external datasets. Despite banks having invested in multiple transaction monitoring systems, having thrown armies of AML analysts and investigators around the process, AML and sanctions related fines have only grown several fold.