A method includes receiving, by a first computer from a second computer, a stateless API request message comprising update value data, a consensus token, and a user identifier. The first computer determines whether or not the consensus token matches a previously stored consensus token. If the consensus token matches the previously stored consensus token, the first computer retrieves stored value data from a value data database based on the user identifier. The first computer then updates the stored value data with the update value data. The first computer generates an API response message comprising an indication of the update. The first computer transmits, to the second computer, the API response message.

In an example embodiment, a method for updating a database with dimensional balancing comprises receiving a transaction request to transfer a portion of a first balance from the first balance to a second balance; determining a subset of the first balance less than or equal to the portion in the first balance compliant with dimensional rules based on one or more attributes of the subset and an identifier of an account holding the second balance; moving the determined subset from the first balance to the second balance; and updating attributes of the moved subset in the second balance.

Embodiments of the invention are directed to systems, methods, and computer program products for adaptive dynamic multi-directional resource transmissions. The invention is structured for dynamically authenticating an activity between network devices, identifying resource deficiencies associated with network devices, and deploying adapted mitigation resources at one or more network devices in real-time, and without requiring discontinuation of the activity. Specifically, the invention is structured to determine a first resource deficiency associated with the first resource. In response, the system is structured to transmit a first adapted mitigation resource of the one or more adapted mitigation resources to the first resource in a transmission direction, triggering transmission of a first resource quantity from the first resource to an entity resource on another transmission direction.

A computing system for secondary merchant card delivery is provided. The computing system may include a processor coupled to a memory to execute secondary merchant card delivery using a secondary merchant card delivery agent of a mobile unit and a merchant card system of an ATM machine in communication with a networked server node. The processor may be operable to receive a user request using the merchant card agent and to send the user request to a server having a merchant card module. In addition, the processor may be operable to initiate a first security protocol and to generate, in response to verified security approval, a merchant card profile. Further, the processor may be operable to send the merchant card profile to the ATM merchant card system, whereby the processor is operable to generate a signal for initiation of generation of the secondary merchant card and dispense the same to the user.

The present disclosure relates to apparatus and methods that allow a Parent or other designated guardian to provide an educational shopping experience to a Child within parameters that protect the Child and Parent. The Parent may indicate a price and details related to the Child's experience, including an event, an amount spent, and an teaching environment engages. Implementations may include a downloadable or pre-installed app, an API or other executable code.

A system for protecting the privacy of consumer assets. The system may include a web-based interface. The web-based interface may include an entity-facing side and a consumer-facing side. The system may include an internal processing executable executing on a processor. The internal processing executable may be obscured from and inaccessible to, both the entity-facing side and the consumer-facing side. The entity-facing side of the web-based interface may prompt the user to provide opt-in permission, receive profile data from the consumer, generate a record for the consumer, enter a minimum asset amount and initiate a funds verification process for the consumer. The consumer-facing side of the web-based interface may receive opt-in permission and receive asset data. The executable may execute a query using on the one or more assets using existing financial rails and/or new financial rails.

Provided is a payment agency server which grants a virtual currency limit on the basis of a crypto-currency collateral providing signal received from the user terminal or the exchange market server, transmits a virtual currency information providing signal to the user terminal or the exchange market server, and processes payment when a virtual currency payment request signal is received from the user terminal or the payment request terminal.

A system and method for settling monetary and quota-allocated dual currency transactions may be used to reduce Co2 emissions. Each purchasable item (product or service) in the system may have a monetary price and a Carbon price whose value corresponds to a number of Kg of Co2 emitted by the manufacture/sale/use of the purchasable item. The system and method individualizes the reduction of Co2 emissions and allows each consumer to choose purchasable items in part based on the Carbon price.

Systems and methods are provided for processing credit token and debit token based transactions in a blockchain supported network.

A transaction card system pulls past transaction data from a financial institution. A user profile model is applied to the past transaction data and customer data features and transforms the data into a user feature profile that summarizes the identity data, financial health and payment capacity of the customer. An overdraft request has current declined transaction details relating to a customer transaction using the transaction card. The computing network pulls from cache the most recent customer transaction banking data and most recent user feature profile and applies a machine learning approval model to the current declined transaction details, the most recent customer transaction data, and the most recent user feature profile, and determines whether to approve or decline the overdraft amount.