Published by Gbaf News
Posted on February 24, 2018
7 min readLast updated: January 21, 2026
Introducing Autonomous Financial Crime Management to the AML Category, the New SAM Solution Detects Complex Financial Crime While Increasing Productivity
Financial services organizations and their compliance programs face the hard realities of meeting regulatory requirements around detecting and reporting anti-money laundering schemes, while managing the cost of compliance. Today NICE Actimize, a NICE (Nasdaq:NICE), business and leader in autonomous financial crime management solutions, takes aim at this challenge with its next generation Suspicious Activity Monitoring (SAM) solution, which combines machine learning analytics for laser-accurate crime detection with robotic process automation, virtually eliminating the manual search for third party data, increasing team productivity, and reducing investigation time for a single alert by up to 70 percent.
The new Suspicious Activity Monitoring solution introduces NICE Actimize’s innovative concept of Autonomous Financial Crime Management to the anti-money laundering category for the first time. NICE
Actimize’s recently-announced Autonomous Financial Crime Management approach represents a massive shift in unifying and mitigating risk through targeted utilization of big data, advanced analytics everywhere, artificial intelligence and robotic process automation which in concert reduce reputational risk.
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:
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.
Julie Conroy, Aite Group
“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.”
Joe Friscia, President, NICE Actimize
“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.”
Introducing Autonomous Financial Crime Management to the AML Category, the New SAM Solution Detects Complex Financial Crime While Increasing Productivity
Financial services organizations and their compliance programs face the hard realities of meeting regulatory requirements around detecting and reporting anti-money laundering schemes, while managing the cost of compliance. Today NICE Actimize, a NICE (Nasdaq:NICE), business and leader in autonomous financial crime management solutions, takes aim at this challenge with its next generation Suspicious Activity Monitoring (SAM) solution, which combines machine learning analytics for laser-accurate crime detection with robotic process automation, virtually eliminating the manual search for third party data, increasing team productivity, and reducing investigation time for a single alert by up to 70 percent.
The new Suspicious Activity Monitoring solution introduces NICE Actimize’s innovative concept of Autonomous Financial Crime Management to the anti-money laundering category for the first time. NICE
Actimize’s recently-announced Autonomous Financial Crime Management approach represents a massive shift in unifying and mitigating risk through targeted utilization of big data, advanced analytics everywhere, artificial intelligence and robotic process automation which in concert reduce reputational risk.
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:
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.
Julie Conroy, Aite Group
“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.”
Joe Friscia, President, NICE Actimize
“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.”
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