Systems and methods for cognitive mobile wallet management are disclosed. In embodiments, a method comprises: obtaining, by a computing device, user contextual data; determining, by the computing device, a potential user purchase and an associated purchase category; and dynamically determining, by the computing device, a flexible spending rule for the purchase category based on one or more stored spending rules and the contextual data, wherein the flexible spending rule changes an amount of currency available to a user through the computing device.

A computer-implemented method includes: determining assets held by a remitter, the assets to be spent in a remittance transaction between the remitter and one or more payees, in which each asset corresponds to a respective asset identifier, a respective asset amount, and a respective asset commitment value; determining a remitter pseudo public key and a remitter pseudo private key; determining a cover party pseudo public key, in which the cover party pseudo public key is obtained based on asset commitment values of assets held by the cover party; and generating a linkable ring signature for the remittance transaction.

As the world progresses towards a cashless payment society, there has been a rise in the various forms of emerging payment technologies. Such technologies may include digital wallet payment systems. There is a need for a bridging protocol and conversion engine that would connect gaps between these various emerging payment technologies and their respective proprietary ecosystems. The conversion engine may be configured to forward currency received from an ecosystem to an account/digital wallet external to the ecosystem.

A method for managing a time-based currency includes steps of: receiving a credit-issuance request containing a user identifier and a value K; based on the credit-issuance request, generating a credit-issuance instruction containing the value K and a user account corresponding to the user identifier; and sending the credit-issuance instruction to a blockchain system, in order for the blockchain system to generate a transaction record in the blockchain, the transaction record indicating that K number of time credits having a base time point corresponding to a time point when the transaction record is generated have been added to the user account.

Data account management systems and methods are described. A server is in communication with one or more information source systems, at least one user device associated with at least one user and one or more electronic data accounts associated with the user. The server links the electronic data accounts, monitors data among the information source systems and the linked accounts for any changes in activity associated with the user, predicts a behavioral activity of the user via machine learning based on at least one of the monitored data and historical data associated with the user, identifies at least one notification instruction specific to the user based on the predicted behavioral activity, and generates an interactive graphical user interface (GUI) for display on the user device. The interactive GUI presents one or more of the notification instruction and a user profile of the at least one user among the linked accounts.

Techniques are disclosed to provide customized loans on a per-user basis. With user permission or affirmative consent, user data may be monitored for several users, which may be used to calculate initial loan specifics such as a loan rate and term based upon a portion of this user input data. The user data may include demographic data, behavioral data, or other data indicative of a user's future potential earnings or other relevant information that may be analyzed to determine, for that specific user, the current likelihood that the user will default on the loan and a future likelihood of default. When this future statistical likelihood is determined, the initial loan specific may be further modified and/or a targeted notification may be sent indicating these customized loan specifics.

Techniques are disclosed utilizing cognitive computing to improve banking experiences. A user's financial account(s) and location may be monitored to determine that a user is near an asset that is listed for sale. The techniques disclosed include receiving multiple locations for a user's mobile device over a period of time and determining when the mobile device is within a predetermined threshold distance of the asset listed for sale. The techniques include building a financial profile for the user based upon financial information for the user. The financial profile may be used to determine a loan to offer to the user for purchasing the asset listed for sale based upon the financial profile.

Techniques are disclosed utilizing cognitive computing to improve banking experiences. A user's financial account(s) may be monitored to predict when a surplus of funds is unnecessarily present and for how long this will likely be the case. Once this is determined, techniques include automatically drafting funds from the account to another account having a higher interest rate where the funds may accrue more interest. The techniques also include predicting when an overdraft may occur and taking appropriate action when such a prediction is made. Predictions may be based upon different weighted inputs used in accordance with a predictive modeling system, which may attempt to predict for a particular user, location, and retailer, whether the user will spend an anticipated amount in excess of the user's current balance. If so, passive (e.g., notifications) and active (e.g., transferring cover funds) actions may be performed.

A method for maintaining database integrity comprises: receiving event data from a producer; converting the received event data to a standard set of ledger entries; publishing the converted entries to a database; determining if two accounts in the database balance out; performing a remedial action to maintain the database integrity if the accounts do not balance.

A proof of wallet approach is used for transaction validation for a digital currency. When a transaction is requested, a set of witness nodes are selected to form an ad hoc neural network. The witness nodes may be client devices of other users of the digital currency. Each witness node receives input information about the transaction (e.g., an encrypted amount and nonce) and neural network parameters (e.g., input weights and a bias). The input information passes through the ad hoc neural network, which generates an output validation value. The transaction is approved if the output validation value is consistent with a verification value generated from the transaction parameters, neural network parameters, and digital currency information stored on a blockchain. If the transaction is approved, the transaction is added to the blockchain in conjunction with the identity of the witness nodes and any other pertinent information