Actimize Ltd.

Debabrata Pati

Danny Butvinik

Pravin Dahiphale

Akshaykumar Bhausaheb Tilekar

Shevale Ashish Suhas

Ofir Itzhak Reichenberg

A computerized-method for automatically generating a two-part readable Suspicious Activity Report (SAR) from high-dimensional data in tabular form is provided herein. The computerized-method may include receiving high-dimensional data in tabular form of evidence financial transactions to be reported under Anti Money Laundering (AML) regulations. Then, displaying the received data to a Subject Matter Expert (SME) for ordering each displayed transaction in a predefined construction; Then, training one or more Natural Language Generation (NLG) translation models, for each transaction type, according to a deep learning model. Then, operating the one or more NLG translation models on each transaction type to generate for each transaction type a narrative of SAR; Then, operating a prebuilt summary model on the generated narrative of SAR of each transaction type to generate a summary of the plurality of narratives of SAR; and combining the plurality of narratives of SAR and the summary to one SAR.

Automatic generation of a two-part readable suspicious activity report (SAR) from high-dimensional data in tabular form

A computerized-method for generating a dataset for a Machine Learning (ML) model for an increased accurate financial crime detection from an initiation stage of the ML model implementation. The computerized-method includes retrieval of financial transaction records from a database of financial transaction records to arrange a dataset of financial transaction records, according to preconfigured techniques. Then, processing the records in the dataset; Then, operating feature engineering on preselected anomalous related features to yield probabilistic categorical features and to yield probabilistic numerical features, and then combining the probabilistic categorical features with the probabilistic numerical features to generate a complex features dataset, and providing the probabilistic categorical features, the probabilistic numerical features and the complex features dataset to an ML model, thus, increasing accuracy of detection that is performed right from an initiation stage of the ML model implementation.

Probabilistic feature engineering technique for anomaly detection

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

A computerized-method for generating a dataset for a Machine Learning (ML) model for an increased accurate financial crime detection from an initiation stage of the ML model implementation. The computerized-method includes: retrieval of financial transaction records from a database of financial transaction records to arrange a dataset of financial transaction records, according to preconfigured techniques. Then, processing the records in the dataset; Then, operating feature engineering on preselected anomalous related features to yield probabilistic categorical features and to yield probabilistic numerical features, and then combining the probabilistic categorical features with the probabilistic numerical features to generate a complex features dataset, and providing the probabilistic categorical features, the probabilistic numerical features and the complex features dataset to an ML model, thus, increasing accuracy of detection that is performed right from an initiation stage of the ML model implementation.

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

Debabrata Pati