Regulatory expectations have grown significantly in the last 10 years in the anti-money laundering (AML) arena. Financial institutions invest substantial resources in order to meet regulatory requirements and detect illicit transactions. True money laundering activity can be the proverbial needle in a haystack. However, the smart use of technology can both increase the effectiveness of the AML program in detecting suspicious activity and reduce the overall resource burden by eliminating inefficient and manual processes.
Current use of technology within AML focuses on the use of transaction monitoring and know your customer (KYC) systems. Transaction monitoring systems integrate with core banking applications and run a series of rules on customer transaction activity. Transactions which break these rules are flagged for further review, typically through a manual process. Transaction monitoring systems are the only viable method for reviewing the vast number of transactions flowing through financial institutions; however, they do create a significant amount of work since only about 5% of their output is legitimate activity worth investigating.
“Smart programs will continue to embrace technological solutions to refine the detection of potential illicit activity and make it easier for their teams by reducing manual activities that add little value”
KYC systems assist with gathering information on customer backgrounds and behavior. The information stored in the KYC system is used for multiple purposes. First to determine which customers pose a higher risk and require proactive monitoring. Secondly, to serve as an information repository for AML investigators seeking to review customer behavior.
The future of AML technology will utilize artificial intelligence (AI) and robotic process automation (RPA) tools to further reduce manual processes and increase the ability to detect suspicious transactions. RPA can be used to automate the gathering of data from multiple sources, significantly lessening the amount of time investigators must spending on this routine task. RPA tools can generally integrate with legacy systems and be implemented for much less cost and time than a complete system overhaul. These benefits make them an attractive option for rapidly enhancing the AML program.
AI tools are currently unproven in AML programs; however, recent regulatory guidance has encouraged the use of advanced technology. AI tools are able to add an additional layer to transaction monitoring through the use of advanced algorithms to either detect or clear activity based on pattern matching with prior types of transactions and activities which were and were not deemed suspicious.
In addition to embracing new technologies, AML programs must make use of data and analytics to enhance their efficiency and effectiveness. Traditionally, AML personnel have come from law enforcement, legal, and general banking backgrounds. While these skillsets are still useful, employees with technology and data science skills are becoming more in demand. As systems become more complex, employees with technology backgrounds are needed to maintain and operate the tools. In addition, advanced analytical methodologies requiring the use of statistical software packages and “big data” require personnel with the requisite background to employ these appropriately. Cyber incidents are also becoming a part of the AML sphere. Investigating this activity requires additional knowledge to follow the money through complex systems and transactions.
Money laundering is a complex activity that can come in many forms and is difficult to detect, even for a trained investigator. Technology will never fully replace a human review in the AML field. However, smart programs will continue to embrace technological solutions to refine the detection of potential illicit activity and make it easier for their teams by reducing manual activities that add little value.