A cross-entity and cross-retailer platform is provided that captures transaction and item return data, indexes, and stores the data in a cloud-accessible data store. A service is provided that custom processes retailer and entity-defined workflows based on purchase transactions and return transactions using the data store. The entities may comprise manufacturers of an item, a supplier of the item, a distributor of the item, and a Consumer Packaging Goods (CPG) company of the item. In an embodiment, a given entity workflow causes the service on a given return to calculate a custom aggregation of returns for a given item or a given set of items across different retailers within a given geographical region for a select period of time and provide selective data about the returns automatically to an inventory system and/or a schedule/delivery system of the given entity through an Application Programming Interface (API).

Method, system and non-transitory computer-readable medium configured to store instructions for implementing a method for providing automated collateral eligibility services implemented by one or more collateral management service (CMS) devices between a client and at least one other party. The method includes receiving a collateral eligibility schedule setup request from a client device. The collateral eligibility schedule setup request includes one or more attributes and one or more rules. The method further includes initiating a current collateral eligibility schedule based on the received collateral eligibility schedule setup request; transmitting a notification to the at least one other party to review the current collateral eligibility schedule; receiving approval of the current collateral eligibility schedule from the at least one other party; and activating the approved current collateral eligibility schedule.

Systems and processes for the gamification of data, including providing scenarios and actionable elements to execute a preferred scenario. Embodiments can include a credit modification software tool that is configured to automate the transmission of one or more messages to effectuate modification of a credit score associated with a user. The system can determine accounts eligible for improvements, application amounts, time to apply, and determine the effect on user's credit score. Embodiments can include scenarios for increasing a user's credit score where funds are not available by applying for a loan to reduce rolling debts, thereby providing a net increase credit score. Loans can be negotiated based on the resulting credit score and autonomously implement. A universal payment system is disclosed to retrieve data and determine a transaction model, which determines the accounts to be used, in which order, and how much to be applied, in order to benefit a credit score.

A computer-implemented method includes receiving an indicator of enrollment of a user in a breach notification service; acquiring information regarding the user; and generating one or more indicators of a data breach for an entity that stores one of data regarding the user or an indication of a transaction with the user in a past predefined time period. The computer-implemented method further includes determining that the one or more indicators meet a threshold level for notifying the user of the data breach; in response to determining that the one or more indicators meet the threshold, generating a notification specific to the user regarding the data breach; and providing the notification to the user during a log-in process for a product or service associated with the provider computing system.

A platform implementing a two-tier tokenization process to build a digital asset pool at a server. An application builds the digital asset pool, initializes general asset tokens to represent pro-rata ownership interests in a general pool of assets, and uses general asset tokens to create specific asset tokens to represent ownership interests in specific assets from that pool that a user of the platform selects, from a remote device in communication with the server, from the general pool. General asset tokens offered to eligible retail and/or institutional investors generate funding to build the asset pool. Owners of general asset tokens are periodically offered, by the server, the option to select portions of specific assets from the general asset pool, and create through the two-tier tokenization process, shares of specific asset tokens, subject to the technical protocols, ownership concentration limits, and bidding and allocation schema established by the present platform.

Intraday alert volume adjustment based on risk parameters is disclosed. A set of alerts associated with user transactions can be displayed to one or more human reviewers for processing. The set of alerts can be analyzed to determine whether or not the volume of alerts exceeds a threshold capacity level, which is based on a number of human reviewers available to process alerts. If the volume of alerts exceeds the threshold capacity level, alerts in the set of alerts can be prioritized based on risk in descending order and displayed to the human reviewers. In one instance, alerts below a threshold can be hidden from view of the human reviewers and optionally automatically approved. Further, prioritization can be performed dynamically as new alerts are added to the set.

A computer system for identifying merchant category code misclassifications includes at least one processor in communication with a transaction database and a high-risk merchant database. The transaction database stores transaction records by a plurality of account holders. The high-risk merchant database stores high-risk merchant records each associated with high-risk merchants. The processor queries the transaction database for transaction records and calculates a high-risk cardholder metric for each of the account numbers. The at least one processor further queries the transaction database for transaction records including (i) the account number of high-risk cardholders, and (ii) a merchant identifier associated with other than the plurality of high-risk merchants, to retrieve a second set of transaction records. The at least one processor further calculates a high-risk merchant metric for each of the merchant identifiers, identifying a MCC misclassified merchant.

Securely sharing proof of knowledge of information without revealing the information, for example to allow an institution to prove it has knowledge regarding an alert. Code or software may be generated (at a first computer system) which takes as input one or more details regarding an entity and returns a value based on a match to one or more actual details regarding the entity. A name may be generated for the code based on information describing the entity, and the code may be published to a blockchain, along with at least one key corresponding to the code. For each name an entry may be creating in an index, converting the name to a blockchain address. A proof based on the code may be generated at a second computer system, which, if verified, results in the first institution and the second institution sharing information regarding the entity.

Techniques are disclosed relating to transaction authorization. In some embodiments, a server computer system receives and caches browsing information for a device of a user, where the browsing information relates to a transaction service. The server computer system may then receive a request to authorize one or more transactions via the transaction service. The server computer system may evaluate the cached browsing information to determine whether the user is attempting to perform multiple concurrent transactions via the transaction service. Based on the evaluating, the server computer system may determine whether to authorize the one or more transactions. In some embodiments, the disclosed techniques may advantageously prevent or reduce authorization of duplicate transactions that are concurrently attempted by a user.

The present disclosure relates to systems and methods for creating and training neural networks. The method includes collecting a set of signals from a database; applying a transform to each signal to create a modified set of signals, wherein signals of the modified set of signals are wavelets; iteratively, for each of a subset of the modified signals: training the neural network using a modified signal of the subset by adding at least one node to the neural network in response to an error function of an analysis of the modified signal exceeding a threshold; removing nodes from the neural network with activation rates below an activation rate threshold; and grouping each node into a lobe among a plurality of lobes, wherein nodes belonging to a lobe have a common characteristic.