Compare Clovis vs. KROTON

Clovis has been intentionally designed to efficiently integrate with pre-existing data structures, allowing it to seamlessly interact with information from legacy systems and enhance the processing and reporting of alerts with a greater range of capabilities. Additionally, our unique AI-driven solution, ClovisQ, synthesizes various customer attributes—such as demographics, industry specifics, geographical data, and transactional history—to provide businesses with an up-to-the-minute view of data as trends and patterns develop. Our platform offers flexibility, being available for both On-Premises and Software as a Service (SaaS) deployment, enabling organizations to choose their preferred method of managing both labor and technological resources. This strategic model is poised to deliver substantial cost efficiencies for companies of all sizes while simultaneously boosting their revenue potential. By leveraging the invaluable experiences of our founders from major consumer banks, prominent data firms, and the U.S. Intelligence Community, we are pioneering a comprehensive AI-driven anti-money laundering (AML) solution that stands to transform the industry. In doing so, we aim to empower businesses to navigate complexities with ease and adapt to an ever-evolving financial landscape.

H3M KROTON modules seamlessly integrate with traditional rule-based compliance systems, enhancing them through the use of Artificial Intelligence. Rather than merely aiding companies in compliance, H3M shifts the focus, positioning crime detection as a secondary benefit. Unlike most platforms, H3M is architected specifically to identify concealed connections that facilitate crime prevention, thereby yielding superior compliance, improved regulatory relationships, a competitive edge, and increased brand value as beneficial side effects. The KROTON suite boasts four AI-driven modules that effectively merge compliance expertise with data science. H3M KROTON Suspect Miner tackles the significant challenges posed by older systems through its innovative machine-learning strategy that employs active learning techniques. This technology can reduce false positives by two-thirds and consistently identifies new laundering tactics. By integrating the insights of analysts with AI capabilities through active learning, H3M enhances machine performance with proactive labeling, ultimately fostering a more robust compliance framework. As a result, organizations can expect not just compliance, but also a significant transformation in their approach to risk detection and management.