NICE Actimize Launches SAM Solution

NICE Actimize, a leader in autonomous financial crime management solutions, has announced the launch of its Suspicious Activity Monitoring (SAM) solution to address the hard realities of meeting regulatory requirements around detecting and reporting anti-money laundering schemes, while managing the cost of compliance.


NICE Actimize Launches SAM SolutionCombining machine learning analytics for laser-accurate crime detection with robotic process automation, the new Suspicious Activity Monitoring solution virtually eliminates the manual search for third party data, increases team productivity, and reduces investigation time for a single alert by up to 70 percent.
SAM introduces the concept of Autonomous Financial Crime Management to the anti-money laundering category for the first time, representing a massive shift in unifying and mitigating risk through targeted utilization of big data, advanced analytics everywhere, artificial intelligence and robotic process automation. 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.
Joe Friscia–, President, NICE Actimize

Joe Friscia, President, NICE Actimize, said:

NICE Actimize’s Autonomous Financial Crime Management is transforming the anti-money laundering industry’s approach to suspicious activity monitoring by creating a paradigm shift in the way analysts approach their work. With financial services organizations hitting their breaking point, with resources devoted to 80 percent rote administrative work with only 20 percent intelligence, it was critical that we dramatically turn that unproductive scenario around. Our new solution, centered on our commitment to turning our Autonomous Financial Crime Management vision into a reality, automates everything but the analysts’ final decision in every transaction, putting the emphasis on human decision-making instead of manual execution.

SAM offers “intelligent” segmentation, enabling analysts to work with their operations to create more meaningful and accurate customer groups, thereby significantly reducing false positives.
Julie Conroy – Research Director at Aite Group

Julie Conroy, Research Director at Aite Group, commented:

Financial institutions need advanced analytics that can evolve rapidly, and machine learning provides that advantage. Machine learning enables models to learn on an iterative basis and, the success is such that those that do not invest in this technology risk being left behind. Machine learning has already been applied to other challenges facing financial services organizations with positive, quantifiable results and now anti-money laundering applications will benefit from this technology. The beauty of machine learning is that it can be applied to any use case where there is both ample data and a problem to solve.
The new SAM solution features:
  • 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.