Actimize Ltd.

Yoav Avneon

Danny Butvinik

Amir Shachar

Gabrielle Zaghdoun Azoulay

Liat Elboim

Maria Zatsepin

Ganir Tamir

Einat Neumann Ben Ari

Elina Maliarsky

A machine learning (ML) system and methods are provided that are configured to detect concept drift in ML models. 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 drift detection operations which include receiving a first data set for use during online training of a first ML model, determining a change to an uncertainty bound metric associated with classifiers for features utilized by the first ML model, identifying that the first data set causes the concept drift with the online training of the first ML model, determining characterization information about a type of the concept drift, generating an ML update paradigm based on the concept drift and the characterization information, alerting an ML model updater of the ML update paradigm.

Automated concept drift detection in live machine learning systems for machine learning model updating

A risk-prediction-preparation module to generate a risk-prediction-model, is provided herein. The risk-prediction-preparation module includes accessing a data-storage of transactions to operate a group-by operation on transactions related to data-points, according to a logical-entity into entities. Then, clustering entities of a clean-financial dataset into clusters. Selecting data-points of: (a) entities from the clusters to a first dataset and (b) a preconfigured amount of entities randomly to a second dataset. Selecting all entities that have at least one 'fraudulent' data-points in at least one related data-point to add all the entities to the first dataset and the second dataset. Using vectorized and scaled extracted features for training a first machine-learning-model of fraud detection on the first dataset and training a second machine-learning-model of fraud detection on the second dataset to collect results. Using the results for combining the first machine-learning-model and the second machine-learning-model to an ensemble machine-learning-model for risk-prediction.

Clustering-based data selection for optimization of risk predictive machine learning models

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 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.

Real drift detector on partial labeled data in data streams

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.

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 of instances

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Ness Zyiona, Israel

Yoav Avneon