Editorial & Advertiser Disclosure Global Banking And Finance Review is an independent publisher which offers News, information, Analysis, Opinion, Press Releases, Reviews, Research reports covering various economies, industries, products, services and companies. The content available on globalbankingandfinance.com is sourced by a mixture of different methods which is not limited to content produced and supplied by various staff writers, journalists, freelancers, individuals, organizations, companies, PR agencies Sponsored Posts etc. The information available on this website is purely for educational and informational purposes only. We cannot guarantee the accuracy or applicability of any of the information provided at globalbankingandfinance.com with respect to your individual or personal circumstances. Please seek professional advice from a qualified professional before making any financial decisions. Globalbankingandfinance.com also links to various third party websites and we cannot guarantee the accuracy or applicability of the information provided by third party websites. Links from various articles on our site to third party websites are a mixture of non-sponsored links and sponsored links. Only a very small fraction of the links which point to external websites are affiliate links. Some of the links which you may click on our website may link to various products and services from our partners who may compensate us if you buy a service or product or fill a form or install an app. This will not incur additional cost to you. A very few articles on our website are sponsored posts or paid advertorials. These are marked as sponsored posts at the bottom of each post. For avoidance of any doubts and to make it easier for you to differentiate sponsored or non-sponsored articles or links, you may consider all articles on our site or all links to external websites as sponsored . Please note that some of the services or products which we talk about carry a high level of risk and may not be suitable for everyone. These may be complex services or products and we request the readers to consider this purely from an educational standpoint. The information provided on this website is general in nature. Global Banking & Finance Review expressly disclaims any liability without any limitation which may arise directly or indirectly from the use of such information.

NICE ACTIMIZE TRANSFORMS ANTI-MONEY LAUNDERING WITH NEW SUSPICIOUS ACTIVITY MONITORING SOLUTION UTILIZING ROBOTIC PROCESS AUTOMATION AND ARTIFICIAL INTELLIGENCE TECHNOLOGIES

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:

  • 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.

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.”