Abstract: A computerized-method for identifying fraud transactions in transactions classified as legit transactions by a classification Machine Learning (ML) model, in a financial system, is provided herein. The computerized-method includes operating training by: (i) retrieving a dataset of fraud-labeled transactions to train a ML fraud model on the dataset of fraud-labeled transactions, to mark transactions as ‘similar’ or ‘novel’; and (ii) retrieving a dataset of legit-labeled transactions to train a ML legit model on the dataset of legit-labeled transactions, to mark transactions as ‘similar’ or ‘novel’; and deploying a classification ML model, a trained ML fraud model and a trained ML legit model in a computerized environment to identify fraud transactions in transactions which have been classified as legit transactions by the classification ML model. The trained classification ML model has been trained on a dataset of preconfigured transactions from the data store, to mark transactions as ‘legit’ or ‘fraud’.

Abstract: 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.

Abstract: 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, is provided herein. The computerized-method includes: sending the received data to machine-learning models to synthesize patterns of the received data to yield a deferential privacy data; maintaining in the database the deferential 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.

Abstract: 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.

Abstract: 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.