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公开(公告)号：US11755932B2
公开(公告)日：2023-09-12
申请号：US16856071
申请日：2020-04-23
申请人： Actimize LTD.
发明人： Danny Butvinik
CPC分类号： G06N5/04
摘要： 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.
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公开(公告)号：US11328301B2
公开(公告)日：2022-05-10
申请号：US16826241
申请日：2020-03-22
申请人： Actimize LTD.
发明人： Danny Butvinik
摘要： A computerized-method for real-time detection of financial transactions suspicious for money-laundering, by processing high-speed streaming financial data. In a computerized-system receiving a financial data stream comprised of data points. Operating a Fused-Density (FD)-based clustering module that is configured to: (i) read the data points; (ii) maintain a grid system; (iii) maintain one or more provisional clusters (PROC)s; (iv) associate each data point with a grid or merge it to a PROC; (v) systemize the grid system and the PROCs; (vi) trim one or more grids and remove one or more PROCs; (vii) form one or more shape devise clusters based on the PROCs; and (viii) transmit the one or more shape devise clusters for analysis thereof, thus, enabling detection of financial transactions suspicious for money-laundering according to the one or more shape devise clusters which were formed out of the high-speed streaming financial data with money-laundering changing trends.
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3.发明授权公开(公告)号：US11900385B1
公开(公告)日：2024-02-13
申请号：US17899635
申请日：2022-08-31
申请人： Actimize LTD.
CPC分类号： G06Q20/4016 , G06N20/00
摘要： 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.
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公开(公告)号：US11361254B2
公开(公告)日：2022-06-14
申请号：US16798473
申请日：2020-02-24
申请人： Actimize LTD.
发明人： Danny Butvinik , Yoav Avneon
摘要： A computerized-system and method for generating a reduced-size superior labeled training-dataset for a high-accuracy machine-learning-classification model for extreme class imbalance by: (a) retrieving minority and majority class instances to mark them as related to an initial dataset; (b) retrieving a sample of majority instances; (c) selecting an instance to operate a clustering classification model on it and the instances marked as related to the initial dataset to yield clusters; (d) operating a learner model to: (i) measure each instance in the yielded clusters according to a differentiability and an indicativeness estimators; (ii) mark measured instances as related to an intermediate training dataset according to the differentiability and the indicativeness estimators; (e) repeating until a preconfigured condition is met; (f) applying a variation estimator on all marked instances as related to an intermediate training dataset to select most distant instances; and (g) marking the instances as related to a superior training-dataset.
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公开(公告)号：US20240185250A1
公开(公告)日：2024-06-06
申请号：US18075445
申请日：2022-12-06
申请人： Actimize Ltd.
发明人： Sunny THOLAR , Danny Butvinik , Yonit Marcus
IPC分类号： G06Q20/40
CPC分类号： G06Q20/4016
摘要： A computerized-method for generating a classification Machine Learning (ML) model, in a cloud-based environment, is provided herein. The computerized-method includes building an ML model by using different isolated datasets from different environments: (i) identifying tenants of a service-provider by a base-activity; (ii) retrieving a set of features of objects from a database of each identified tenants to detect common features; (iii) using an object storage service in each tenant's environment to retrieve a dataset having the detected common features; (iv) training a ML model to classify objects on each retrieved dataset corresponding to a tenant from the tenants. The training of the ML model is a continuous training where the ML model continues training after each dataset, and then deploying a trained ML model in a target tenant system to classify objects. The target tenant system has no training dataset and no feasible training thereon.
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公开(公告)号：US20230316281A1
公开(公告)日：2023-10-05
申请号：US17712148
申请日：2022-04-03
申请人： Actimize LTD.
CPC分类号： G06Q20/4016 , G06N20/20
摘要： A computerized-method for building ensemble of supervised and unsupervised Machine Learning (ML) models for fraud-predictions, for a client having an extremely-imbalanced-dataset, is provided herein. The computerized-method includes: (i) receiving an extremely-imbalanced-dataset from a client for building a ML model; (ii) retrieving datasets of other clients; (iii) identifying a rate-of-dataset-imbalance for each retrieved dataset; (iv) routing each dataset of ‘K’ datasets with identified rate-of-dataset-imbalance above a preconfigured-threshold to supervised ML models for training thereof and to yield a trained-object; (v) training a meta-learning-supervised ML model by providing the ‘K’ yielded trained-objects; (vi) routing each dataset of ‘L’ datasets with identified rate-of-dataset-imbalance below a preconfigured-threshold to an unsupervised ML model to generate clusters; (vii) combining the ‘k’ supervised ML models and the ‘L’ unsupervised ML models into ensemble ML models; and (viii) deploying the ensemble ML models in a financial-system in production-environment for prediction of fraud in a financial-transaction.
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公开(公告)号：US11531903B2
公开(公告)日：2022-12-20
申请号：US16945876
申请日：2020-08-02
申请人： Actimize LTD.
发明人： Ganir Tamir , Danny Butvinik , Yoav Avneon
摘要： A computerized-method for real-time detection of real concept drift in predictive machine learning models, by processing high-speed streaming data. The computerized-method includes: receiving a real-time data stream having labeled and unlabeled instances. Obtaining a window of ‘n’ instances having a portion of the ‘n’ instances as reliable labels. Computing posterior distribution of the reliable labels; and operating a Drift-Detection (DD) module. The DD module is configured to: operate a kernel density estimation on the computed posterior distribution for sensitivity control of the DD module; operate an error rate function on the estimated kernel density to yield an error value; and train an incremental estimator module, according to the kernel density estimation. When the error value is not above a preconfigured drift threshold repeating operations (i) through (iii), else when the error value is above the preconfigured drift threshold, at least one concept drift related action takes place.
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公开(公告)号：US20220108133A1
公开(公告)日：2022-04-07
申请号：US17063731
申请日：2020-10-06
申请人： Actimize LTD.
发明人： Debabrata PATI , Pravin Dahiphale , Danny Butvinik
摘要： 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. 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.
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公开(公告)号：US12124933B2
公开(公告)日：2024-10-22
申请号：US16866139
申请日：2020-05-04
申请人： Actimize LTD.
发明人： Amir Shachar , Einat Neumann Ben Ari , Danny Butvinik , Yoav Avneon , Gabrielle Zaghdoun Azoulay , Liat Elboim
CPC分类号： G06N20/20 , G06N20/00 , G06Q20/4016 , G06F21/60
摘要： 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.
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公开(公告)号：US11954174B2
公开(公告)日：2024-04-09
申请号：US17063731
申请日：2020-10-06
申请人： Actimize LTD.
发明人： Debabrata Pati , Pravin Dahiphale , Danny Butvinik
IPC分类号： G06F16/25 , G06F18/214 , G06F21/62 , G06N3/045 , G06Q50/26
CPC分类号： G06F18/2148 , G06F16/254 , G06F21/6245 , G06N3/045 , G06Q50/26
摘要： 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.
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