Enhancements from the leader in business analytics software and services include predictive analytics, an enriched regulatory reporting console, and additional risk-based scoring techniques.

"Enhancing the quality of alerts by applying predictive analytics is a significant process improvement over the typical transaction monitoring approach," said Rodney Nelsestuen, senior research director at CEB TowerGroup. "This helps institutions lower false positives, reduce analyst fatigue, and improve the quality of investigations all of which are increasingly important in the face of tougher regulatory actions and stiffer fines for poor anti-money laundering (AML) monitoring."

SAS Anti-Money Laundering has unique predictive analytics improving alert quality while minimizing false positives, leading to a more effective and meaningful investigation. This helps escalate alerts to investigation more accurately, so investigators can focus on the most egregious risks, while reducing costs associated with model governance.

The updated regulatory reporting console in SAS Anti-Money Laundering supports multiple forms and languages, and interacts with new electronic filing systems being adopted by global financial intelligence units. An improved user interface speeds investigations by displaying all relevant customer information based on user roles, rights and privileges, to display key performance indicators of the most recent trends.

Banks can harness risk-based scoring during the onboarding process with new features in SAS Anti-Money Laundering. The risk classification process regularly assesses a customer's risk profile in a single data pass, based upon transactional behavior or association with high-risk attributes. That means institutions can monitor high-risk peer groups uniquely. End users can modify or create unlimited scenarios through a point-and-click interface, monitoring more risks and behaviors while bypassing trusted accounts and parties posing no threat.