Abstract: Systems and methods are provided for providing alerts to a user. The systems and methods may include a financial service provider including a memory device storing instructions. The financial service provider may also include at least one processor configured to execute the instructions to perform a plurality of operations. The operations may include receiving data relating to an activity of a user. The operations may also include identifying a merchant based at least on the received data. The operations may also include accessing historical fraud or disputes data associated with at least one of the user and the merchant. The operations may also include determining whether the received data triggers an alert. The operations may further include sending an alert message to a user device associated with the user when the processor determines that the received data triggers the alert.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for providing network security. The system may receive customer credentials in association with an attempted transaction initiated by a user device that is connected to a local network. The system may receive network identification data associated with the local network and generate a network confidence score based on the network identification data and a historical local network footprint. The system may determine a security action based on the network confidence score.

Abstract: An artificial intelligence system for communicating predicted hours of operation to a client device. The system may include a processor in communication with a client device and a database; and a storage medium storing instructions.

Abstract: In a method for providing provisioning information, a central data processing system receives from a transaction data processing system, an encrypted user datum associated with a client user of the transaction data processing system; receives from at least one of a plurality of account administrator data processing systems, a response comprising a notification that a user account administrated by that account administrator data processing system is associated with the client user; receives an account administrator selection message including identification of a user-selected account administrator from an account administrator list; transmits to the account administrator data processing system associated with the user-selected account administrator, a provisioning request for client user account provisioning information; receives from the account administrator data processing system associated with the user-selected account administrator, the client user account provisioning information; and transmits to the t

Abstract: A system for securely updating an electronic transaction card held by an account holder with an additional account and/or account data is described. A dynamic transaction card may be securely updated with an additional account by using pre-stored shell data and/or inactive data, whereby the pre-stored shell data may be populated using data received from an issuer system and/or the inactive data may be activated via an activation signal received from a mobile device. A backend server may determine, via a fraud determination, expiration determination, and/or user-request, that new account data should be transmitted to an account holder. The dynamic transaction card may receive the data associated with a notification, update a display, instruct an EMV applet to use a key associated with a received EMV key identifier for signatures, and/or update any additional data stored on the dynamic transaction card.

Abstract: In certain embodiments, machine learning and lineage data may be used to manage data. In some embodiments, a computing system may use lineage data to identify two datasets that may be related. The computing system may determine that a user has access to a derivative dataset but does not have access to an original dataset that was used to create the derivative dataset. In response, the computing system may use a machine learning model to generate a similarity score indicating a level of similarity between the original dataset and the derivative dataset. If the similarity score satisfies a threshold score, the computing system may modify access rights of the user so that the user is unable to access a portion of the data in the derivative dataset.

Abstract: A banking pipeline for processing various transactions for multiple financial instruments is disclosed herein. The pipeline may have three distinct interpreters, a transaction interpreter, a rollup interpreter, and a rules interpreter. Different aspects of the transaction may be performed on separate interpreters and each interpreter may perform its aspect of the transaction before the next interpreter begins.

Abstract: Provided are compact transaction cards. In some approaches, a body of the card includes first and second sections rotatably coupled together, wherein the first and second sections define a first main side opposite a second main side, an insertion end perimeter opposite a second end perimeter, and a first side perimeter opposite a second side perimeter. The body may include a chip along the first main side, the chip being located on the first section of the body. A magnetic stripe may be provided along the second main side of the body, wherein the magnetic stripe is located on the second section, wherein the magnetic stripe extends parallel to the insertion end perimeter when the first and second sections are in a first configuration, and wherein the magnetic stripe extends perpendicular to the insertion end perimeter when the first and second sections are in a second configuration.

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

Abstract: Methods and systems are described herein for improvements for reducing client-server network traffic. For example, methods and systems allow for the reduction of client-server network traffic by altering search filters based on their respective rank and/or frequencies of being searched with other search filters included in a search query. In this way, users do not have to resubmit queries that are illogical or would return a null set of search results. For example, a first query may be received. A first search may be performed based on the first query. A first search filter may be selected based on a hierarchical tree structure of search filters. A second query may be generated comprising the first search filter and one or more other search filters, wherein the second query comprises fewer search filters than the first query, and a second search may be performed based on the second query.

Abstract: Methods and systems are described herein for facilitating data integrity processes using measures of statistical dispersion (e.g., gini impurities) of dataset features. The described mechanism may be also be used for selection and dimensionality reduction. Dimensionality reduction may enable storing the dataset using less storage space or performing other operations on the dataset using less resources. In some embodiments, the above described mechanism may be used for supervised categorial clustering and/or categorical classification.

Abstract: The invention relates to executing an action for a cryptographic storage application by comparing a movement dataset, which may contain positional and inertial datasets, with a user profile dataset. The system may receive a first cryptographic storage application address, retrieve a first movement dataset corresponding to a first time period, retrieve a user profile dataset, compare the first movement dataset with the user profile dataset, and execute an action for the first cryptographic storage application.

Abstract: Embodiments may generally be directed to systems and techniques to detect failure events in data pipelines, determine one or more remedial actions to perform, and perform the one or more remedial actions.

Abstract: Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process.

Abstract: Disclosed are methods and systems for generating a virtual tag for an item in a physical environment. For instance, an image of the physical environment captured by and displayed on a user interface of a computing device may be provided as input to a trained machine learning model configured to identify the item including a first subset of item attributes. The item may be identified and information associated with the item, including a second subset of item attributes, may be received from a data store using the first subset. A spatial location of the item may be identified in the image. An icon may be generated for the item and rendered by the computing device for display on the user interface in association with the spatial location of the item in the image. The icon may be indicative of at least one item attribute from the first or second subset.

Abstract: Methods and systems are described herein for novel uses and/or improvements in application or service authentication for a provider of disparate applications and/or services using blockchain technology. As one example, a method and a system are described herein for a unified user device authentication mechanism across multiple lines of service using cryptographic tokens. The system may receive, as part of a login request from a user, an address associated with a cryptography-based storage application, a cryptographic signature, and an identifier associated with a cryptographic token. The system may verify that the cryptographic token is valid and that the cryptography-based storage application controls the cryptographic token to grant the user access to the application or line of service.

Abstract: Systems, methods, articles of manufacture, and computer program products to: train a prediction model using a machine learning process, the prediction model configured to estimate whether further application of a hyperparameter tuning technique will cause an improvement in at least one of the hyperparameters; select the hyperparameters using the tuning technique; apply the prediction model to determine if further adjustment of the hyperparameters is likely to improve the success metric; and terminate the tuning technique when: accuracy of the prediction model in predicting improvement in a hyperparameter is above a predetermined accuracy threshold, and the prediction model predicts that further application of the tuning technique will not result in an improvement to the hyperparameter; or the accuracy of the prediction model in predicting improvement in the parameter is below the predetermined accuracy threshold, and an accuracy of hyperparameter adjustment is determined to be below a predetermined adjustment

Abstract: Methods, systems, and devices for leveraging data already collected on a user in a secure and private manner, in particular to verify user credentials for third parties. The methods, systems, and devices innovate beyond traditional security and privacy platforms in computer systems by processing the data to create a useable metric for the purposes of the third parties, in which the useable metric preserves the security and privacy of the underlying data.

Abstract: A device may include a processor, a wireless transceiver in communication with the processor, and a non-transitory memory. The memory may store instructions that, when executed by the processor, cause the processor to perform processing. The processing may include receiving a command configured to be executed by an external user device. The external user device and the user device may be members of a network of trust. The processing may include detecting, by the wireless transceiver, a plurality of member devices of the network of trust in communication range of the device. The processing may include sending, by the wireless transceiver, the command to each of the plurality of member devices.