Fmr Corp.

Amin Assareh

Siddharth Narayanan

Peter Theodoros

Michael Noto

Charles Voelker

Joshua Marine

Wenlu Yan

Methods and apparatuses are described for automated customer engagement prediction and classification. A server generates a feature vector comprising variables corresponding to historical user activity data for a user. The server encodes, for each feature vector, each variable in the feature vector into a corresponding weight-of-evidence value. The server transforms each encoded feature vector into an embedding in a multidimensional vector space. The server generates, for each user, a user engagement probability value by identifying embeddings of other users in proximity to the user embedding using a similarity measure and determining an engagement outcome for the identified embeddings. The server assigns each user to an engagement probability cluster based upon the engagement probability value for the user. The server generates instructions for a remote device to initiate communications to each user based upon the assigned engagement probability cluster.

Automated customer engagement prediction and classification

Systems and methods for detecting anomalous data traffic over proxy servers in a data communications network. The method includes receiving, by a server computing device, network log data corresponding to data traffic during a timeframe. The method further includes normalizing the network log data using at least one of timestamp data of the network log data or IP address data of the network log data. The method also includes extracting risk-based data features from the network log data. The method further includes calculating using an isolation forest algorithm, anomaly scores for the normalized network log data based on the extracted risk-based features. The method also includes determining at least one anomaly event based on the calculated anomaly scores. The method further includes identifying at least one host device and at least one timestamp corresponding to the at least one anomaly event.

Systems and methods for data exfiltration detection

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Boston, MA, United States of America

Amin Assareh