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 – AML compliance. 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 over $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 systems, integration issues with monitoring systems and the analysis involved for the multitude of transactions and tracking against internal and external datasets. Despite banks having invested in multiple transaction monitoring systems and thrown armies of AML analysts and investigators around the process, AML and sanctions-related fines have only grown multi-fold.
According to Compliance Week, banks and financial institutions have been hit with $10.4 bn in global fines and penalties related to AML, KYC, data privacy and MiFID regulations in 2020, bringing the total to $46.4 bn for those types of breaches since 2008.
Banks have used various techniques to identify money laundering but these solutions have not resulted in a significant reduction in false positives rather increased false negatives. Banks cannot afford to keep throwing resources at this process as the compliance costs only mount.
Tt. Most banks already use a set of relatively simple monitoring systems to screen transactions for illicit conduct. Some of these systems screen currency transactions to identify those which indicate “structuring”— a series of transactions designed to evade current reporting requirements (e.g., five deposits of $3,000 each in a single day). Other systems monitor wire transfers to look for countries or individuals that appear on a list compiled by Treasury’s Office of Foreign Assets Control (OFAC).
The problems with these traditional monitoring systems and algorithms is that they are primarily rules- based and generate thousands of false positives and false negatives making it impossible to manually investigate all true cases in a timely manner.
Time for a Reset
Thanks to cognitive technologies like AI and machine learning, various tools are now available to detect true money laundering cases automatically and efficiently. These tools combine big data analytics and machine learning in identifying patterns of behavior, aggregating across and fusing together a massive spectrum of data sources. They translate structured and unstructured data into signals to detect intent and behavioral anomalies, establishing what is called normal behavior (based on a historical transaction set) and then identifying anomalous behavior and subsequently disentangling true anomalies from false positives. The result is an exponential reduction in false positives and false negatives, while banks can employ a much smaller number of compliance analysts to investigate the reduced cases before filing SARs.
It can be a difficult decision for the Chief Compliance officers (CCO) of banks and financial services firms to decide how to move forward after investing millions of dollars in earlier transaction monitoring systems. The great thing about the AI-based systems is that they are complimentary to the existing rules-based systems and can be implemented on top of existing systems in a matter of weeks. FinCEN itself has been using artificial intelligence systems for over a decade to monitor all the CTRs received from banks.
The business case for the is very simple – not only do you avoid penalties but also safeguard a bank’s reputation in global markets.
AI and machine learning automation are the perfect technologies to solve the complex, massive and laborious task of AML compliance.
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