Actimize Ltd.

Pravin Dahiphale

Debabrata Pati

Danny Butvinik

Ofir Itzhak Reichenberg

A computerized-method for scaling automatic deployment of a machine-learning detection model in a cloud-based managed analytics service by knowledge sharing to overcome an imbalanced dataset learning problem. The computerized-method includes: sending the received data to machine-learning models to synthesize patterns of the received data to yield a differential privacy data; maintaining in the database the differential privacy data of one or more on-prem cloud-based managed analytics services to generate a consortium shared synthetic data lake; operating phases of machine-learning detection model based on the received data and data in the database to create a packaged model. The data in the database is aggregated and used during the operating phases of the machine-learning detection model to create a packaged model for other on-prem cloud-based managed analytics services, thus overcoming imbalanced dataset learning thereof, and after the packaged model is created it is automatically deployed on-prem.

Sharing financial crime knowledge

In a method for detecting unauthorized or suspicious financial activity, a graph convolutional network for financial crime prevention, a separate node is created for each entity: each account, each person, each address (e.g. email address), etc. Separate attributes are provided to aggregate transactions in which the node acts as a sender; transactions in which the node acts as a receiver; transactions using a specific channel (e.g. ATM); and transactions of a specific type (e.g. online money transfer). In some embodiments, the attributes exclude data on individual transactions to reduce the amount of data and hence provide more effective computer utilization. The approach is suitable for many applications, including anti-money laundering. Other features are also provided, as well as systems for such detection.

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Pune, India

Pravin Dahiphale