A computer-implemented method for allowing purchase of virtual gift cards includes receiving a communication indicating a gift card type and specifying a value amount. Responsive to the communication, a purchase of a virtual gift card of that type and storing that value amount is processed. This processing includes processing a payment for the specified value amount. Further, a gift card merchant application programming interface (API) capable of processing a purchase of a virtual gift card of the indicated gift card type is identified from amongst a set of such APIs. That API is used to initiate purchase of the virtual gift card. After the purchase is processed, an indication of the purchased virtual gift card is sent to a mobile computing device. The mobile computing device uses the indication to configure itself to allow transactions using the purchased virtual gift card to be initiated. Fraud detection may also be performed.

Methods and systems are presented for automatically verifying online content for different device configurations and/or account configurations. A request for verifying a user interface software workflow is received from a device. The request can specify particular parameters and content to see if that content appeared correctly when presented to users. Session data associated with one or more real-world user interaction sessions between user devices and a service provider server is obtained. The session data is used to generate data representing how one or more user interface elements are rendered on one or more user devices during the one or more real-world user interaction sessions. The data is comparable against benchmark data to determine if content was correctly presented. Reporting data can be made available that indicates if the user interface workflows are operating correctly.

A method for handling one or more items in an item storage in a communication network. The method includes initiating a procedure for handling one or more items in an item storage based on an input related to a user in the communication network, and selecting a position related to an item of the one or more items based on the input. Further, the method includes guiding the user to the selected position via triggering a guiding indication associated with the selected position, and detecting a pattern change in a registered pattern, wherein the pattern change is due to movement of an item associated with the selected position or movement of another item. Upon detection of the pattern change, a confirmation indication is sent for confirming handling of the item back to the user.

Methods, systems, and apparatuses are described herein for protecting user accounts using machine learning models. A machine learning model may be trained to determine whether account activity indicates a risk to credit scores. Account data, associated with a first financial account, may processed to determine whether the first financial account is associated with at least one underage user. A transaction request, associated with the first financial account, may be received. A history of transactions conducted by the first financial account may be retrieved. The trained machine learning model may be provided, as input, the transaction request and the history of transactions. An indication of risk to a credit score associated with the at least one underage user may be received as output from the trained machine learning model. A limitation may be added to the first financial account based on the indication of risk.

A method of employing fraud rules associated with identification of fraud in connection with financial transactions may include receiving information associated with a fraud scenario and defining a fraud rule based on the information. The fraud rule may include fraud criteria used to analyze financial transaction data to detect the fraud scenario and may also include a fraud response. The method may further include defining activation criteria for the fraud rule and enabling activation of the fraud rule for inclusion in product flows associated with the financial transactions in response to the activation criteria being met.

A method for detecting fraudulent transactions entering a payment environment, the method comprising: receiving packets of a transaction from a network; reconstructing and framing the packets into respective transaction messages; decoding each transaction message into its respective fields; correlating the respective transaction messages into an end-to-end model of the transaction; applying one or more predefined rules to the respective fields to determine whether the transaction is fraudulent; when the transaction is determined to be fraudulent, determining one or more specified fields of the respective fields to use to selectively block, deny, or rate limit the transaction; selecting a corresponding predefined rule from a server rule base; storing the predefined rule in a transaction firewall rule base; and, applying the predefined rule to the transaction to selectively block, deny, or rate limit the transaction based on content of the one or more specified fields in the transaction.

A payment and authentication network may include a communications interface, one or more processors, and a memory. The memory may have instructions stored thereon that, when executed by the one or more processors cause the one or more processors to receive, using the communications interface, transaction information associated with a transaction from a merchant system and generate a fraud risk score based on the transaction information. The instructions may cause the one or more processors to determine that the fraud risk score is indicative that the transaction is likely fraudulent and transmit an alert to a user device that informs a user of the user device that the transaction is likely fraudulent prior to generating an approval decision for the transaction.

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. Using the results for combining the first machine-learning-model and the second machine-learning-model to an ensemble machine-learning-model for risk-prediction.

A computer implemented method for enabling a payment transaction in a computer system executed at least partially by a computer of a user connected via a communication network to a remote computer comprising a database of payment-related data, said method comprising the steps of: a) accessing the payment-related data of a user from within the database, wherein a web browser extension on the user's computer is invoked by the user clicking on a web browser extension graphical user interface (GUI) widget displayed on the user's computer, wherein secure network access is established to access the user's payment-related data from within the database; b) presenting the user's payment-related data via a pop-up window displayed on the user's computer, without requiring application programming interface integration with a website or a web application of a payee, and without requiring a form re-direct away from the website or the web application of the payee; c) enabling the payment transaction, wherein the web browser extension invokes an auto-complete function to automatically populate fields of a checkout form presented from the website or the web application of the payee with the user's payment-related data.

There are provided systems and methods for dynamic autoscaling of server resources using intelligent demand analytic systems. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing resource to provide computing resources to users. During use of such computing resources by end users and their computing devices, different demand and needs may be required by such users and devices. The service provider may utilize an intelligent machine learning system to predict computing resource needs and demands at different future time periods based on past usages over similar time periods, computing requests and demands, and network communications. The machine learning engines may identify one or more usages curves, which may be of one or more degrees of curvature, to determine potential future usage. Using these past analytics, the service provider may dynamically scale automatic provision of computing resources.