Question: Is there a solution that can help reduce false positives in AML risk detection and improve compliance efficiency?

ComplyAdvantage has an AI-based system designed to reduce false positives and optimize compliance workloads. It automates manual processes, real-time transaction monitoring, and red flags from new accounts, known typologies and trends in customer transactions. The result is a big reduction in manual labor and false positives, and it's a good option for companies that want to improve their AML risk assessments and compliance.

Nasdaq Verafin is another all-purpose system that pools data from hundreds of institutions for better security and efficiency in fraud detection and AML/CFT. It combines machine learning with consortium analytics to cut false positives and speed up investigations. The system also offers a secure way for financial institutions to share information with each other for joint investigations, so it's a good option for institutions that want to improve their financial crime management.

Flagright has an AI-native AML compliance and fraud prevention system with automated case management, real-time transaction monitoring and AI-based risk scoring. It offers detailed AI-based insights and case management tools that can reduce manual work hours by 40%, false positives by 80% and productivity by 200%. The system is designed to help fintechs, banks and other financial services companies better monitor and manage financial crime.

SEON combines advanced digital footprinting, machine learning and customizable rules to help companies avoid fraud and stay compliant. Its real-time user activity monitoring and AI-powered insights help to automate compliance processes and improve fraud detection accuracy. With more than 5,000 businesses using SEON, it's a scalable option for companies of all sizes looking to automate fraud checks and stay compliant.