Anti Money laundering continues to be a major issue in the financial ecosystem but the methods are changing drastically in this technology-driven era. The Bank Secrecy Act (BSA) was introduced long back, mandating the reporting of suspected fraud or laundering. However, now the Financial Crimes Enforcement Network (FinCEN) has modified and expanded the anti-money laundering (AML) requirements. Financial organizations now have rules, outlined by the Department of the Treasury agency, to include reporting cyber-enabled crime and cyber-events through Suspicious Activity Reports (SARs). Apart from including the questionable activity by customers, the updated rules also include suspicious activity on the bank level.
Need for Machine Learning and Analytics
Threats are increasingly becoming multidimensional and this leads to the need for machine learning for detecting the fraud. Anti money laundering faces another challenge that is the fraud never translates into the activity of one transaction, business, person or account. And, detecting all the cases in a timely manner isn’t possible for a person. This calls for analytics-based security that uses machine learning to detect everything that humans cannot. It also helps figure out the complete sequence of activities needed for SAR fillings. Including information on cybersecurity events or breaches in the SARs also ensures that the financial firms are careful about cyber threats. Also, law enforcement can have adequate information for detecting fraud.
Developing Effective Strategies
Considering the present scenario, anti money laundering/Bank Secrecy Act (AML/BSA) officers must have the knowledge and stay updated about technology and cyber threats. Also, there must be regular meetings between AML/BSA team and the information security team of the institution. It is also essential for the AML/BSA team to review the incident response plan of the banks for addressing cyber-events and cyber-enabled crimes. This helps in establishing the role of AML/BSA team in the event handling processes.
Identification of common patterns and trends associated with suspicious activities can be done by tracking and reviewing cyber cases. It’s also important to understand the key similarities among such cases by using data analytics and aggregation tools. Such enhanced analysis and reporting can help the law enforcement in developing their cases better. FinCEN suggests that to determine if a cyber-event needs to be reported or not, a financial organization must consider all the available information surrounding the event, including the systems and information targeted as well as the nature of the cyber-event.
While investigating a case, the financial institution must gather all the information from its information security team and customers that may have been the victims of the cyber attack. It may also create a case collection form to collect specific information about cyber events. This would help in guiding the information security team to capture specific information to fill the SAR. Collecting more and more information helps banks to assist law enforcement effectively in developing and investigating the cases and identifying industry trends.
AML/BSA officers must review FinCEN’s advisory in order to work with the information security team of the institution for developing effective plans to address and report potentially adverse cyber-events. An effective program needs proper identification, collection, and documentation of all the information related to the cyber events, followed by sharing the information with the law enforcement.