NICE Actimize Cloud-Based Platforms Achieve Best-In-Class Ranking In 2021 “Aite Matrix: Leading Fraud & AML Machine Learning Platforms” Vendor Report
NICE Actimize’s cloud-based platforms secured best-in-class ratings across vendor stability, client strength, and product features categories against 11 financial crime industry vendors
NICE Actimize, a NICE business, has been named a leader in Aite-Novarica Group’s “2021 Aite Matrix: Leading Fraud & AML Machine Learning Platforms” vendor landscape report, securing best-in-class ratings in the categories of vendor stability, client strength and product features in a field of 11 vendors across primary financial crime categories. The vendors were evaluated via the Aite Matrix, Aite-Novarica Group’s highly governed and quantitative vendor evaluation methodology.
Recommended AI ML Article: Do’s, Don’ts and Legalities Involved in Future Brand Collabs
The two NICE Actimize platforms cited in the report included the X-Sight Financial Crime Management Platform as-a-Service and the Xceed Integrated AML & Fraud Platform. Built for enterprise scalability, X-Sight extends best-in-class financial crime risk management with core services powered by the cloud. Also powered by the cloud, Xceed brings together best-in-class AML and fraud solutions, offering complete financial crime and compliance on a single platform.
According to the Aite-Novarica Group report, “With its “AI First” approach, NICE Actimize is dedicated to infusing advanced analytics across the entire client risk life cycle. Tailored to different market segments, NICE Actimize’s agile and scalable platforms aim to deliver elevated datasets and intelligence, drive better financial crimes risk management, and lower the total cost of ownership.”
The Aite-Novarica Group report also stated that, “NICE Actimize is a leading provider of enterprise software solutions for financial crime and is well known for developing innovative technology to protect institutions by identifying financial crime, preventing fraud, and providing regulatory compliance. NICE Actimize combines deep industry expertise and a patented technology platform to quickly enable global businesses to increase their insight into real-time customer behavior and improve risk and compliance performance. NICE Actimize provides enterprise risk management solutions to banks, insurance companies, payment companies, and government entities in 70 countries.”
Recommended AI News: Enjinstarter to Launch 20 IDOs in December 2021
According to Chuck Subrt, Fraud & AML practice director, Aite-Novarica Group and co-author of the report, “Among the most significant developments of the evolving financial crime market are the emergence of fraud and AML detection solutions that provide FIs with the capability to optimize the performance of their controls by way of applying advanced analytical techniques to discover, develop, test, deploy, and tune highly customized detection logic and policy administration. These machine learning platforms and ecosystems have created a new segment of the market for fraud and AML detection solutions. Our report reflects that NICE Actimize is a market leader in integrating these advanced AI and machine learning into its solutions.”
“NICE Actimize continues to deliver advanced analytics across the entire client risk lifecycle. By delivering agile, scalable platforms tailored to different market segments, NICE Actimize solutions drive industry-leading financial crime risk management and lower total cost of ownership,” said Craig Costigan, CEO, NICE Actimize. “We thank Aite-Novarica Group for recognizing our innovations in artificial intelligence and machine learning.”
“To support financial crimes detection and prevention, NICE Actimize’s solution suite leverages advanced analytics such as machine learning, automation, and NLP, from the data layer, through insights, through decisioning to investigation and reporting,” the Aite-Novarica report noted. “NICE Actimize uses several machine learning algorithms in its models, depending on the business need—both supervised (e.g., XGBoost, random forest, and regression) and unsupervised (e.g., clustering and isolation forest).”
Recommended AI News: OctoML Unveils Next Iteration Of ML Deployment Platform To Scale ML Operations
[To share your insights with us, please write to email@example.com]