MASTERCARD TECHNOLOGIES CANADA ULC

Devices, methods, and computer-readable media that perform frictionless multi-factor authentication. In one embodiment, a server includes a memory including a frictionless multi-factor authentication (fMFA) service, and an electronic processor communicatively coupled to the memory. The electronic processor is configured to receive a frictionless multi-factor authentication (fMFA) request and collected data of a user interface device, determine, with a unique device identifier (UDID) component, a unique device identifier (UDID) of the user interface device based on device attributes included in the collected data, determine, with a passive biometrics component, a biometrics match assessment against a historical profile, determine whether a user of the user interface device is authenticated under multi-factor authentication (MFA) based on the UDID and the biometrics match assessment, responsive to determining that the user is authenticated under multi-factor authentication (MFA), output a fMFA authentication response that indicates authentication of the user.

The disclosure relates to methods and systems of automatically generating rules based on an evolutionary algorithm. A system may generate an initial population of rule candidates bound by a respective constraint. The system may perform a crossover of the initial population of rule candidates with one another to generate a plurality of crossover child rules and may apply a probabilistic mutation to at least some of the plurality of crossover child rules. The system may evaluate performance of the plurality of crossover child rules based on one or more fitness evaluation metrics that measure performance of a given crossover child rule and select a subset of the plurality of crossover child rules based on the evaluated performance. The system may repeat the process until a specified number of evolutionary generations is reached.

Systems, methods, and non-transitory computer-readable media for forming an extensible data warehouse. A data ingestor application receiving raw data having a first structure. Forming a data lake in the third memory using the raw data. Continuously receive additional raw data having a plurality of structures. The plurality of structures including the first structure and one or more different structures. The additional raw data supplementing the raw data. Determining each structure of the plurality of structures. Generating a dataset based on the additional raw data and the plurality of structures that are determined. Extracting metadata associated with the additional raw data from the dataset. Creating a catalog of the dataset based on the metadata that is extracted. Modifying the data lake in the third memory to include the additional raw data based on the dataset and the catalog.

A system includes memory hardware configured to store instructions and one or more electronic processors configured to execute the instructions. The instructions include logging historical behavioral biometric metadata from one or more computing platforms, generating a user profile based on the logged historical behavioral biometric metadata, receiving an authentication request from a client computing platform, providing the first behavioral biometric metadata and the user profile to a trained machine learning model to generate a biometric match, generating a positive control signal in response to a positive biometric match, sending the positive control signal to the client computing platform, updating the historical behavioral biometric metadata with the first behavioral biometric metadata, and retraining the trained machine learning model using the updated historical behavioral biometric metadata. The authentication request includes first behavioral biometric metadata.

Examples manage chargeback requests between issuers and acquirers. A method includes identifying an issuer and an acquirer involved in an active dispute case, computing a first count of wins for the issuer at a particular stage in a dispute resolution process using historical dispute cases; computing a number of resolved cases at the first stage; computing a win percentage for the issuer based on the first count of wins and the number of resolved cases; weight the win percentage; add the weighted win percentage with other weighted win percentages, thereby resulting in a dispute score; and transmitting the dispute score for display to one of the issuer and the acquirer, the dispute score representing a likelihood of one of the issuer and the acquirer in prevailing in the active dispute case over the other of the issuer and the acquirer.

Devices, methods, and computer-readable media that perform sessions correlation for multiple device authentication. In one embodiment, a server may include an electronic processor communicatively coupled to the memory, the electronic processor is configured to: establish a first session with a first user interface device, establish a second session with a second user interface device that is different from the first user interface device, receive, from a third-party server, a score request including information that correlates the second session with the first session, generate a score on a correlation of the first session to the second session, and output the score indicating a level of multiple device authentication, the score also being a control signal for authorizing or denying a multi-factor authentication at the third-party server.

Methods and systems for providing offline fraud assessments with respect to customer login data. One system includes an electronic processor configured to receive an electronic file. The electronic processor is also configured to perform an offline batch processing on the electronic file, where the offline batch processing includes performing a fraud evaluation on the electronic file using a ruleset. The electronic processor is also configured to determine a fraud assessment for the electronic file based on the fraud evaluation. The electronic processor is also configured to generate and transmit a fraud report for display, the fraud report including the fraud assessment.

Disclosed are example embodiments of systems and methods for use in identify content included in datasets, independent of certain data being included in the datasets. In an example embodiment, a computer-implemented method generally includes accessing interaction data as a dataset, where the interaction data is representative of multiple network interactions and includes a first variable and a second variable, and appending demographic data to the dataset. The method also includes applying an exponential decay function, based on multiple constants, to the first variable of the dataset, where each of the constants is indicative of a different defined interval, and encoding the second variable of the dataset into multiple columns in the dataset, where each of the multiple columns includes a binary value. The method then includes training a classifier model based on the dataset, where the demographic data defines classification of the interaction data.

A computer-implemented method, includes generating a plurality of task-specific embeddings at a plurality of task-specific encoders based on a plurality of input data structures, aggregating the plurality of task-specific embeddings to generate an aggregated embedding, applying a dimensionality-reduction technique to the aggregated embedding to the aggregated embedding to generate a final embedding, and providing the final embedding to a user device for use in a machine learning application.

A system includes and non-transitory computer-readable media storing instructions an electronic processor configured to execute the instructions to receive a screening payload from a client platform, the screening payload identifying an authorization request, identify a stored enhanced authorization request corresponding to the identified authorization request, the stored enhanced authorization request including first device characteristics, identify a device on a network having second device characteristics, the second device characteristics matching the first device characteristics, and transmit a control signal to the identified device, the control signal configured to cause the identified device to generate an alert.