Symphony AyasdiAI

 

AyasdiAI introduced unconventional philosophies (primarily pharmaceutical, oncological and intelligence communities) to transform the discovery of hidden behaviors, relationships and criminal malignancies within financial institutions. 

This low impact design utilizes multiple AI disciplines - unsupervised to automatically increase the “yield” of information from existing data, leveraged by a patented set of fully explainable and focused semi and supervised behavioral models to maximize data fidelity. A fully interoperable solution with the existing model development/management, enables consistency across the anti-crime function. 

SensaAML was developed as a platform to work alongside legacy transaction monitoring systems (TMS) and pave the way for the organization's transition to a more modern approach to AML and fraud management. 

This powerful detection engine has a simple mission to help banks transform how they detect, investigate and interdict financial crime by tracking evolving customer behavior as intuitively and effectively as possible. 

SensaAML, through our cloud-native, microservices, and API-oriented design approach, can sit alongside your existing detection system, providing augmented detection to increase risk coverage through AI and increase operational efficiency with AI-based alert reductions. In addition, because our system is lightweight and agile, implementation times are significantly reduced (typically 3 months) to ensure quick and seamless ROI.

Our revolutionary approach to holistic customer risk scoring has delivered uncovered hidden risk at a 93% hit rate, reduced false positives by 60%, increased risk detection by 120%, and improved speed to risk detection by 40% with customer's banking data.  

The idea of using static rules and scenarios to analyze behavior to monitor and detect today's crime successfully is unrealistic, unsuccessful, and archaic. By continuously tracking Anomaly Detection, Change in Behavior and L3/SAR Similarity within TMS data, SensaAML can tune and prioritize the clients detection models to provide enhanced coverage for known and unknown risks.