Relationship Analytics, Social Network Analysis
Behavioral Science & Deep Content Analytics
Wrapped in Machine Learning
Catelas is the only solution to take a truly Holistic Surveillance approach to Surveillance. We not only integrate trade events, with communications and security data but we also leverage a combination of sciences to triangulate our understanding of the risk associated with people, their communications and their behavior. Machine Learning is deployed throughout the application to help create closed loop learning patterns deep within each algorithm. The net effect is a technology that better understands your world each day and helps the compliance analysis visualize how a firm does business and where the risks lie.
Our patented algorithms automatically uncover how people connect in large networks, the strong relationships within them and who matters. At the core of all our applications are the patented algorithms that look to understand People, the strength and nature of their Relationships, and Human Behavior. Human behavior is universal – how relationships grow and strengthen, the level of trust developed between people, and behavioral patterns are the same the world over. Our technology can uncover behavioral signatures indicative of actions such as fraud, vendor kick-back, and information theft. Our software can even predict when an employee will resign before they resign – irrespective of whether that employee is American, French or Chinese.
To do this we analyze the ‘Meta-data’ of communications such as email, IM, chat rooms etc., and examine the patterns of Interactivity between each person over time. Meta-data includes fields such as the Sender, the Recipients, the date / time stamp etc. and not the content of the message – we don’t care what you are saying or in fact doing. The fact that we don’t use the content to understand relationship strength or other types of behavioral attributes mean we are both language and culture independent. Catelas does not count emails. Instead, the software looks at how employees interact over time and we use this to identify ‘shared experiences’ between people.