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: An autonomous risk investigation system and methods are provided that are configured to automate investigation tasks during a plurality of investigation stages using an intelligent decision automation framework.

Abstract: A computerized-method for comprehensive text-summarization quality-assessment with rank-based normalization and weighted-hierarchical-ranking strategy. The computerized-method includes: (i) receiving an original-text and a summary-text that has been generated by a (GPT)-based LLM that has been provided the original-text and a text-prompt; (ii) operating a text-processing NLP module on the original-text and the summary-text to yield a processed-text of both; (iii) measuring the summary-text to assess text-summarization-quality thereof by operating a plurality of metrics to yield a metric-score; (iv) operating ranked-based normalization on each metric-score to yield a normalized-score; and (v) operating an aggregation based on weighted-hierarchical-ranking strategy of the normalized scores to yield an interpreted final-quality score.

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 for determining when an update of an online ML model is required. The computer-implemented method includes: (i) receiving a batch of financial transactions data; (ii) selecting a set of features from the one or more features; (iii) detecting a drift and a drift type in each feature in the selected set of features, by operating a drift detection model thereon; (iv) generating a batch-representation-vector of drift type for each feature in the selected set of features; (v) receiving a predicted-decision of update-needed by forwarding the generated batch-representation-vector to a trained MetaBDMM model, the predicted-decision of update-needed is one of: update-needed; and update-not-needed, and (vi) forwarding the predicted-decision of update-needed to the online ML model.

Abstract: A machine learning (ML) system and methods are provided that are configured to provide a performance evaluation of an online ML model using an evaluation framework with an offline ML model. 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 model comparison operations which include accessing, for a performance evaluation of a first ML model, a second ML model using the evaluation framework, determining a batch size for the performance evaluation, calculating first model scores for an analytical metric during an online run using the batch size, calculating decayed weights applied to the first model scores, comparing the first model scores with second model scores for the second ML model, and outputting the performance evaluation based on the comparing.

Abstract: A rule training system and methods are provided that are configured to generate machine learning rules for fraud detection based on an automated feature selection by a rule creation system. 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 rule creation operations which include accessing a training dataset, performing a data preprocessing and a data sampling on the training dataset, obtaining a plurality of features from the processed and sampled dataset using a feature engineering operation, selecting a subset of features from the plurality of features using simulated annealing operations, generating the plurality of detection rules using the subset of features, iteratively selecting from the plurality of detection rules, and evaluating a rule performance of each rule in those selected.

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: 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.

Abstract: A computerized-method for identifying synthetic identity fraud operating a financial-activity in a digital financial account, in a Financial Institution (FI), is provided herein. The computerized-method includes building a Machine Learning (ML) model and then implementing the ML model and a synthetic identity identification module in an FI system to evaluate if a financial-activity operated through each account is having a synthetic identity behaviour or a genuine behavior. When a financial-activity is operated through an account, sending the financial-activity to the synthetic identity identification module and operating the synthetic identity identification module and the ML model to provide a calculated synthetic identity fraud score and when the calculated synthetic identity fraud score is above a preconfigured threshold, the financial activity is alerted and the synthetic identity fraud score is sent to an analyst that investigates the financial activity.

Abstract: 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.

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.