Machine learning platforms are an important technology that businesses are turning to in their fight against financial crime, according to the latest Aite Group report.
Financial crime is a lucrative business for organized crime rings, terrorists, and rogue nation states. The stakes are equally high for the financial institutions, processors, retailers, and corporations that are the target of escalating attacks. Machine learning systems represent the next generation of detection and mitigation, and they provide a way for businesses to harness one of their greatest assets—their customer data—and apply advanced analytical techniques that can evolve with the rapid pace of financial crime.
Aite Group’s newest report, AIM Evaluation: Fraud and AML Machine Learning Platform Vendors, explores the key trends within the machine learning platform market for fraud and AML use cases and discusses the ways in which the technology is evolving to address market needs and challenges.
“The ability to effectively address fraud and AML issues while keeping customer friction to a minimum is increasingly a competitive differentiator for financial institutions, processors, and merchants,” says Julie Conroy, research director for Aite Group. “A new breed of technology—machine learning platforms—is gaining steam. These engines enable businesses to harness internal and external data and apply advanced, iterative analytics to detect fraud and money laundering across a variety of use cases,” she adds.
Leveraging the Aite Impact Matrix (AIM), a proprietary Aite Group vendor assessment framework, this Impact Report evaluates the overall competitive position of each vendor, focusing on vendor stability, client strength, product features, and client services. This report evaluates ACI Worldwide, BAE Systems, Bottomline Technologies, Brighterion, DataVisor, Featurespace, Feedzai, FICO, Nice Actimize, SAS, Simility, ThetaRay, and ThreatMetrix, and it profiles Risk Ident.