Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computerized-method for generating high-quality synthetic fraud-data based on tabular-data of financial transaction.

Abstract: 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.

Abstract: The present disclosure provides a machine learning system and method configured to induce neuron activity in a neural network of the machine learning system. Each of the system and method selects a neural network with a multilayer perceptron and performs incremental learning cycle on the multilayer perceptron. An input neuron is modified by strengthening connections between the input neuron and additional neurons. A second input neuron may be modified by weakening connections between the second input neuron and additional neurons. Activation functions associated with the neurons in the multilayer perceptron may be adjusted. Batches of data are run through the multilayer perceptron until a set constraint is met, at which point a prediction is generated for each of the batches from input data from the neural network.

Abstract: A computerized-method for analyzing financial data to improve performance of a concept-drift-detector that is providing alerts of drift to an update component of a machine learning model for fraud prediction and detection, is provided herein. The computerized-method includes retrieving a time-series data of financial transactions having one or more features, during a time unit. For each feature, detecting a process of values of the feature to determine a type of the process. When the type of the process of a feature is determined as nonstationary, determining a subtype thereof and if the process is feasible for rectification to a stationary process, rectifying it. When the type of the process is determined as stationary, determining its subtype and when the type of the process is determined as nonstationary and the process is not feasible for rectification, forwarding the time-series data, the type of the process and the subtype to the concept-drift-detector.

Abstract: 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.

Abstract: A computerized-method for maintaining ethical Artificial-Intelligence by generating a representative-training-sample-dataset for a fraud-detection Machine-Learning (ML) model, by: (i) operating a representative-dataset-preparation module to generate a representative-training-sample-dataset by operating balanced-sampling on randomly-selected preconfigured-number of financial-transactions. The balanced-sampling may be operated by applying a configurable-rule on at least two values of a parameter of non-sensitive PII parameters of each financial-transaction by a low-frequency value; (ii) training the fraud-detection ML model on the representative-training-sample-dataset; and (iii) deploying the trained fraud-detection ML model in a finance-system in test-environment, and operating the trained fraud-detection ML model on a stream-of-financial-transactions to predict a risk-score for each financial-transaction.

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 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.

Abstract: 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.

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: 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

Abstract: 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.

Abstract: 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.

Abstract: 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 e 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 narrative of SAR; and combining the narrative of SAR and the summary of narrative of SAR to one SAR.

Abstract: 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.

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: 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.