Abstract: A computerized system and method may process and detect anomalies in input data using of machine learning models and techniques. A computerized system comprising one or more processors, a memory, and a communication interface to communicate via a communication network with remote computing devices, may be used for assembling a signal based on event data items; calculating an anomaly score for the signal, which may describe a change or difference between the signal and past signals; generating an alert based on the calculated score; presenting the alert on an output computer display; and allowing or reversing data transfers performed over a communication network between physically separate computer systems based on the anomaly score. Some embodiments of the invention may include performing peer anomaly detection context anomaly detection as two separate and distinct anomaly detection procedures, using separate and distinct machine learning models and algorithms.

Abstract: A system and method may detect rogue trading by detecting a subset of trades among a plurality of trades, where each trade in the subset does not meet a trade surveillance system threshold, and does meet a trade surveillance system threshold within a tolerance, and each trade falls within the same time period. A ratio of the subset of trades to the plurality of trades may be determined. If the ratio is above a threshold, it may be determined that the subset of trades corresponds to undesirable trading. Undesirable trading may be determined using an additional factor, based on a weighted average of, for each of a trade surveillance system threshold, the number of trades in the subset meeting the trade surveillance system threshold within a tolerance and not meeting a trade surveillance threshold, times a weight based on the position of the threshold in the trade surveillance system.

Abstract: 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.

Abstract: A computerized-method for real-time detection of anomalous data, by processing high-speed streaming data. In a computerized-system receiving a data-stream comprised of unlabeled data points, and operating an Anomalous Data Detection (ADD) module. The ADD module receives at least one of: (i) k number of data point neighbors for each data point; (ii) X number of data points in a predetermined period of time; (iii) d number of dimensions of each data point, threshold; and (iv) n number of data points that said ADD module is operating on, in a predefined time unit. Then, the ADD module prepares a dataset having n data points from the received X data points; and then identifies one or more data points, from the received data stream, as outliers to send an alert with details related to the identified outliers, thus, dynamically evaluating local outliers in the received data stream.

Abstract: A system and method is provided for automatically creating machine learned fraud detection models. Data received from a plurality of devices can be used to train a model for each of the plurality of entities. Each of the models can be trained using recursive model stacking and each model can output a corresponding score. A second model can be trained for each of the plurality of entities based on the first model and a corresponding output score of the first model. The second model can also be trained using recursive model stacking.

Abstract: A computerized method for providing a sentiment score by evaluating expressions of participants during a video meeting is provided herein. The computerized method comprising: a Sentiment Analysis (SA) module. The SA module is: (i) retrieving one or more recordings of a video meeting from the database of video meeting recordings of each participant in the video meeting and associating the one or more recordings with a participant; (ii) dividing each retrieved recording into segments; (iii) processing the segments in a Facial Expression Recognition (FER) system to associate each segment with a timestamped sequence of expressions for each participant in the video meeting; and (iv) processing each segment in an Artificial Neural Network (ANN) having a dense layer, by applying a prebuilt and pretrained deep learning model, to yield a sentiment score for each statement for each participant.

Abstract: A device, system, and method for training a machine learning model using incremental learning without forgetting. A sequence of training tasks may be respectively associated with training samples and corresponding labels. A subset of shared model parameters common to the training tasks and a subset of task-specific model parameters not common to the training tasks may be generated. The machine learning model may be trained in each of a plurality of sequential task training iteration by generating the task-specific parameters for the current training iteration by applying a propagator to the training samples associated with the current training task and constraining the training of the model for the current training task by the training samples associated with a previous training task in a previous training iteration, and classifying the samples for the current training task based on the current and previous training task samples.

Abstract: Systems and methods for unsupervised feature selection for online machine learning are provided. Features can be selected from a plurality of online data sources having a plurality of respective online data streams, and an aggregated feature set and aggregated data can be formed therefrom. The aggregated feature set and the aggregated data can be used by machine learning models in real time to provide real time online machine learning.

Abstract: A computer-implemented method and system for financial risk assessment are provided. The method includes receiving financial data related to an international monetary transaction from a transferring country to a receiving country; determining a universal currency for the receiving country based on at least one purchasing power parity (PPP) value related to the receiving country; and assessing the financial risk of the international monetary transaction based on the universal currency and the financial data.

Abstract: A system and method for collecting, filtering, analyzing, distributing and effectively utilizing highly relevant events (such as key business events) in real time, from huge quantities of data. The present invention analyzes both historic and real-time data stemming from operational activity, by interfacing with internal data repositories (such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM)), external data sources (such as suppliers and clients), and real time operational systems in order to create an Active Intelligence Platform. This Active Intelligence Platform is positioned as a layer between the organization's data sources and its applications, monitoring inputs and relaying only the important data items to the relevant individuals and/or systems. This allows individuals and systems to respond immediately and effectively to key events.

Abstract: A system and method for collecting, filtering, analyzing, distributing and effectively utilizing highly relevant events (such as key business events) in real time, from huge quantities of data. The present invention analyzes both historic and real-time data stemming from operational activity, by interfacing with internal data repositories (such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM)), external data sources (such as suppliers and clients), and real time operational systems in order to create an Active Intelligence Platform. This Active Intelligence Platform is positioned as a layer between the organization's data sources and its applications, monitoring inputs and relaying only the important data items to the relevant individuals and/or systems. This allows individuals and systems to respond immediately and effectively to key events.