A computer system for numeric translation within digital financial transactions comprises various obfuscation tables and remote systems that each manage particular portions of private data. Each remote system utilizes a unique obfuscation layer to protect private data from being communicated to other systems.

Systems, computer-readable media, devices, and methods for synchronizing user data of a user. The method can include identifying a recognition unit corresponding to a first state of a plurality of states and to a linked merchant of a plurality of linked merchants or to a provider. The method can include generating actionable elements with content corresponding to the recognition unit, and the actionable elements can correspond with a state change from the first state to a second state based on a merchant-provider exchange parameter. The method can include providing the actionable elements to a GUI, receiving a selection corresponding with a request to update states, and updating the first state to the second state. Updating can including deducting the recognition unit from a first account, calculating an equivalent recognition unit value based on the merchant-provider exchange parameter, and allocating the recognition unit to a second account.

An information processing method causing a computer to execute processing of acquiring demand-supply information related to demand or supply for crypto assets on a block chain system; and restricting, based on acquired demand-supply information, a transfer quantity of tokens that can be converted into the crypto assets held by a user.

In a system and method for providing a points liability forecast, data associated with transactions related to a retail loyalty program based on points accumulated by each customer enrolled in the retail loyalty program is received and stored. One or more training sets of data is created based on the received and stored data. The one or more training sets are used to generate a machine-learning model that forecasts points liability. Input parameters related to retail loyalty program are received from aa user, for input to the machine learning model. Forecast parameters based on the input parameters are received, as output from the machine learning model. Finally, the forecast parameters are provided to the user via an interface.

In some examples, a method is included for converting and exchanging loyalty points. The method includes generating, by a computing system, records that include relationship information linking a credit card of a credit card issuer to loyalty rewards program entities. The method includes converting card issuer points of the issuer to loyalty rewards program points of one loyalty rewards program entity of the program entities according to a point conversion rule. The loyalty rewards program points can be issued by the one loyalty rewards program entity to a corresponding loyalty rewards program account of a cardholder of the card after the conversion and according to the point conversion rule. And, the conversion of the card issuer points to the loyalty rewards program points can include a two-step conversion including converting the card issuer points to intermediate points and then converting the intermediate points to the loyalty rewards program points.

Systems, apparatuses, methods, and computer program products are disclosed for distributing digital rewards between third parties. An example method includes receiving rewards data representative of at least a digital rewards profile comprising a digital rewards amount for a digital rewards program of a user and receiving a rewards mapping request comprising instructions to identify one or more available digital rewards sources compatible with the digital rewards amount. The example method further includes identifying one or more digital rewards sources comprising one or more of a second digital rewards profile comprising a second digital rewards amount, a promotion of the digital rewards program, or an incentive multiplier and causing transmission of at least one of a predefined digital rewards amount or the promotion of the digital rewards program. The example method further includes executing a purchase transaction for a product associated with a product cost of the digital rewards program.

Methods and systems are provided for associating Media Access Control (MAC) addresses with public chargers of electric vehicles, also known as Electric Vehicle Supply Equipment (EVSE), using a statistical approach and leveraging big data from connected vehicles (CV) and charge point operators (CPO). The data products generated by the method described herein can be utilized by electric vehicles (EV) to identify the charging EVSE during a public charging event.