Next-generation AML: Eliminating false positives in anomaly detection
Authored by: Lisa Toth, Hatstand (a Synechron company)
With cyberterrorism on the rise, concerns about global terrorist financing and increasing incidences of illegal trading activity, financial services firms (perhaps more than ever) need to leverage the power of automation to identify and stop issues in real-time.
Money-laundering, in particular, continues to be a topic de-jur, with Aite Group estimating in a recent report that the Global AML Software Market will grow to $1.56 billion by 2019 and year-over-year spend consistently on the rise to meet the need for AML suspicious activity monitoring technology and profile solutions. As banks and financial institutions are held increasingly accountable for addressing financial crime or market abuse, they must overcome the challenge of defining a 'behavioral anomaly' in order to then detect it and act on it.
Merely defining an anomaly is a challenge – what is it, and how can it be programmatically identified can be more difficult than it sounds. This requires significant expertise, human resources and time and cannot be scaled to cover scenarios across larger population domains. In addition, more data and more sophisticated applications bring levels of analytic complexity that are immensely intricate.
To find an anomaly, firms need a technological process capable of 'learning', adapting and defining the ever-changing shape of 'normalcy' in order to reliably detect an anomaly. As the research shows, banks are increasingly using automation, or “automation with a human touch,” a term coined in 1988 by Toyota; to monitor for certain conditions and stop activity automatically or based on human intervention in real-time or near real time, when an anomaly is detected.
Technology must be capable of identifying the anomaly in the shape of normalcy and operate under the premise that what you seek does not want to be found. This will only ever be feasible if it is able to autonomously acquire and integrate data across all structures, combining sources in order to build logical knowledge bases around the behavioral phenomena it identifies.
According to Lisa Toth, Global Head of Regulation and Risk at Hatstand (a Synechron company), “Suspicious activity reporting and reliable anomaly detection is a huge issue for tier one banks, regional banks and intermediaries alike and surveillance technology is critical to modern security programs. Next-generation surveillance tools can automatically weed out false positives by looking at each data element in relation to what is considered normal. This is extremely powerful when compared with behavioral analysis systems of the past. Having the ability to detect true anomalies will allow compliance teams to more rapidly resolve issues and shore up controls so that the aberrant behavior does not persist.”