Abstract: A system and method for analyzing data transfers using pattern mining, including: categorizing sequences of events into categories based on an order of the events in the sequences; identifying, for one or more sequences in a given category, subsequences of events in a dataset of event data using one or more data mining algorithms; and accepting or denying a data transfer based on applying logical rules to the data transfer, where the rules may be determined using the identified subsequences. In some embodiments, event sequence categories may include an order sensitive category and an order insensitive category, and identifying one or more subsequences of events may include applying a first data mining algorithm to order sensitive sequences, and applying a second data mining algorithm to order insensitive sequences; rules may be determined based on calculating metrics for identified subsequences, describing occurrences of the subsequences in the dataset of event data.

Abstract: A system is adapted to automatically identify suspected fraudulent transactions. The system includes a fraud management server configured to perform these operations: receiving unlabeled transactions, each having a number of features, and storing them in a transaction repository; with the features, determining a risk score for each transaction; based on the risk scores, dividing the unlabeled transactions into bins in order of their risk scores; labeling transactions of the first bin legitimate and those of last bin as fraudulent; with the labeled transactions, training a first machine learning model; with the trained first machine learning model, labeling transactions of a second bin and a second-to-last bin as either fraudulent or legitimate; storing the labeled transactions of the first bin, second bin, second-to-last bin, and last-bin in the transaction repository; and with the labeled transactions of the first bin, second bin, second-to-last bin, and last-bin, training a second machine learning model.

Abstract: A system and method for analyzing data transfers using pattern mining, including: categorizing sequences of events into categories based on an order of the events in the sequences; identifying, for one or more sequences in a given category, subsequences of events in a dataset of event data using one or more data mining algorithms; and accepting or denying a data transfer based on applying logical rules to the data transfer, where the rules may be determined using the identified subsequences. In some embodiments, event sequence categories may include an order sensitive category and an order insensitive category, and identifying one or more subsequences of events may include applying a first data mining algorithm to order sensitive sequences, and applying a second data mining algorithm to order insensitive sequences; rules may be determined based on calculating metrics for identified subsequences, describing occurrences of the subsequences in the dataset of event data.

Abstract: A system and method for automatically training a machine learning model may include a computing device; a memory; and a processor, the processor configured to: use of one or more subgroups of decision variables of a first machine learning model to train one or more candidate models; evaluate performance metric of one or more candidate models against the first machine learning model: when the performance metric of one or more candidate models is higher than the performance metric of the first machine learning model, update the first machine learning model to a second machine learning model selected from one or more candidate models.

Abstract: A device, system and method for machine-generated automatic fraud detection using a large language model to generate a human-readable summary to detect anomalies in a user's transaction history behavior. A prompt may be input into a large language model comprising a set of features of the user's current and past transactions and instructions to generate a summary explaining deviation in the user's behavior between the current and past transactions. The summary may be analyzed to detect if the deviation in the user's behavior is anomalous. When the analysis detects deviant behavior patterns between the user's current and past transactions, fraud may be suspected to automatically trigger a preventative anti-fraud action, e.g., to pre-emptive cancel, delay execution or escalate interrogation, of the current transaction.

Abstract: A rule training system and methods are provided that are configured to automatically generate machine learning (ML) rules for intelligent decision-making by a policy manager platform. 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 training operations which include accessing rule training data, iteratively generating a plurality of decision rules based on the rule training data and a plurality of ML model training techniques, testing each of the plurality of decision rules, filtering the plurality of decision rules by corresponding performances, selecting a set of the plurality of decision rules based on alert metrics, evaluating the set of the plurality of decision rules, and generating a decision ruleset for the ML task.

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.