Actimize Ltd.

Gabrielle Zaghdoun Azoulay

Danny Butvinik

Yoav Avneon

Amir Shachar

Liat Elboim

Einat Neumann Ben Ari

Shubhanshu Sharma

An artificial intelligence system configured to detect anomalies in transaction data sets. The system includes a processor and a computer readable medium operably coupled thereto, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform modeling operations which include receiving a first data set for training a first machine learning model to detect anomalies in the transaction data sets using a machine learning technique, accessing at least one micro-model trained using at least one second data set separate from the first data set, determining risk scores from the first data set using the at least one micro-model, enriching the first data set with the risk scores, and determining the first machine learning model for the enriched first data set using the machine learning technique.

Artificial intelligence system for anomaly detection in transaction data sets

A computerized-method for predicting a probability of fraudulent financial-account access, is provided herein. The computerized-method includes a. building a Machine Learning (ML) sequence model; b. implementing a forward-propagation-routine in an encapsulated environment that runs applications to mimic a process of the ML sequence model. The forward-propagation-routine is mimicking processing of a chronical-sequence of a preconfigured number of non-financial activities sequence vector, layer by layer to generate a fraud probability score and using weights and biases which were extracted from each layer of the trained ML sequence model; and c. exporting the extracted weights, biases to a persistent storage and converting the forward propagation routine to an executable for integration with a Fraud Management System that is operating the integrated executable to predict a probability of fraudulent financial-account access by providing a fraud probability score to each chronical-sequence of a preconfigured number of non-financial activities.

Computerized-method and system for predicting a probability of fraudulent financial-account access

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Tel Aviv, Israel

Gabrielle Zaghdoun Azoulay