Methods and systems are presented for identifying different users who share a user account with an online service provider and dynamically processing transactions for the user account differently based on which user initiates the transaction request. In some embodiments, an account decomposition system may decompose the user account into distinct users who share the user account. The account decomposition system may identify different users who are sharing a user account by analyzing past transactions associated with the user account and different user devices that were used to conduct the past transactions. The account decomposition system may determine different user profiles for the different users, and may use the different user profiles to process incoming transaction requests initiated by different users of the user account.

Aspects described herein may relate to methods, systems, and apparatuses for applying machine learning techniques as part of registering, for a user, a payment card with at least one account of a merchant. The machine learning technique may be an unsupervised learning classifier that is configured to determine classifications of merchant groups and/or user groups. Based on a classification, the user may be able to select which merchants to register the payment card. Based on the selection, the payment card may be registered with the user's account at the selected merchants. Further, the registration may be performed based on virtual payment card information configured for use with the merchant. The virtual payment card information may be configured to initiate transactions only with the merchant.

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Systems and methods for counter resynchronization can include one or more servers each including a memory and one or more processors. The one or more servers can be in data communication with a transmitting device. The one or more processors can be configured to determine one or more reset events. The one or more processors can be configured to generate a resync value. The one or more processors can be configured to transmit, via one or more scripts, the resync value to the transmitting device according to one or more prioritization factors and in response to the one or more reset events. The one or more processors can be configured to replace the counter value with the resync value in accordance with the one or more prioritization factors.

Methods and systems are presented for identifying different users who share a user account with an online service provider and dynamically processing transactions for the user account differently based on which user initiates the transaction request. In some embodiments, an account decomposition system may decompose the user account into distinct users who share the user account. The account decomposition system may identify different users who are sharing a user account by analyzing past transactions associated with the user account and different user devices that were used to conduct the past transactions. The account decomposition system may determine different user profiles for the different users, and may use the different user profiles to process incoming transaction requests initiated by different users of the user account.

An integrated selling system platform includes a point-of-sale application and middleware to perform global tokenization orchestration, that in combination, provide credit card reading device agnostic global token and provisioning operations. The middleware includes communication interfaces, each of which corresponds to a particular communication protocol. The middleware receives a token create request from the point of sale application and directly communicates, via one of the communication protocols, to a point-of-interaction payment device, to receive and securely encrypt the credit card number, then transmit that information to a hosted network token provisioning system to request a network token. Upon receiving the network token, the middleware communicates directly, via one or more protocols, to a global merchant tokenization host, to create a merchant specific token, to be returned by the middleware back to the point-of-sale client to use as a card-on-file payment method.