New suspicious activity monitoring solution uses AI technologies

NICE Actimize, a provider of financial crime, risk and compliance solutions for regional and global financial institutions, has launched its next-generation Suspicious Activity Monitoring (SAM) solution, which combines machine learning analytics for laser-accurate crime detection with robotic process automation.

Leveraging NICE Actimize's experience in advanced analytics and transaction monitoring solutions, the ultimate goal of SAM is to leverage intelligence and automation to reduce human effort and error, meeting regulators' requirements to detect and report sophisticated crime schemes.

Key elements of the new SAM solution also include:

Expert-infused machine learning: While financial crime analysts provide oversight to the process, the solution's machine learning models work to enhance detection and reduce false positives.

Analytics agility: Automated tuning and optimization keeps AML analytics faster and more flexible than fast-changing financial crime attack patterns and money laundering schemes.

Managed analytics and information-sharing: Cloud-managed analytics takes the burden of model tuning and optimization off financial services organizations. Meanwhile performance dashboards using cloud-based data provides organizations with insight into the performance of their SAM analytics and lets them compare those to industry peer organizations.

Virtual workforce: Robots will assume the rote tasks associated with AML operations, freeing up financial crime experts to focus on the more complicated elements of an investigation.

Visual storytelling: A simple graphical view of money laundering cases means investigators no longer spend hours constructing the stories behind suspicious activity reports.

The new Actimize SAM solution transforms the suspicious activity monitoring process in other critical ways. Offering "intelligent" segmentation, SAM enables analysts to work with their operations to create more meaningful and accurate customer groups, thereby significantly reducing false positives. Once an issue has been found, via an entity-centric view, the solution is able to offer both macro and micro views of issues, through its intuitive user interface.