Abstract: The present disclosure tracks purchases and rewards for users of credit cards, debit cards or other trackable accounts. When a user is ready to purchase an item, such as an item at a retail store, a terminal at the store contacts a system for information concerning credit cards, debit cards and other accounts of the person. Information at the store discloses the category of the item and the price. The system then calculates potential rewards to the user, depending on the card or other account used to pay for the item. The user or the clerk at the store can then select a card or other account to pay for the item, e.g., a credit card or an account of the user with the store or seller of the item. A similar method can be used when purchasing on line, obtaining the price and checking for rewards.

Abstract: A method for providing a savings opportunity includes receiving an authorization request for a user transaction by a first remote computer. By analyzing, at the first remote computer, the authorization request, it can be determined if the currency being used during the transaction is different from the preferred currency (as indicated by a user profile). If there is a difference, a savings opportunity indication for the foreign transaction is generated. The savings opportunity indication may be transmitted to a second remote computer where a savings opportunity from a database of savings opportunities is matched to the user. Then, at least one of the user and the merchant is notified by the second remote computer of the savings opportunity. Alternatively, the matched savings opportunity is returned to the first remote computer for transmittal to at least one of the user and the merchant along with an authorization of the transaction.

Abstract: Disclosed herein is a method of classifying financial transactions by usage patterns of a user. The method includes analyzing metadata extracted from the information associated with financial transactions in accordance with at least one business rule. Where the analysis includes sequentially analyzing the metadata using a constant-time lookup data structure, a Radix tree, a Lucene tree and fuzzy logic methods, until a unique identifier is found that is associated with the metadata. The metadata with the unique identifier is then added to the constant-time lookup data structure to update the constant-time lookup data structure. The transaction data is then classified based on the unique identifier.

Abstract: Disclosed herein is a method for providing users with saving opportunities of interest. The method includes developing a predictive model of user purchase behavior, evaluating a plurality of available savings opportunities from a savings opportunity data base using the model of user purchase behavior, selecting from the plurality of savings opportunities one with the highest likelihood of acceptance and communicating the selected savings opportunity to the user.

Abstract: Disclosed herein is a method is provided for managing campaign effectiveness by a merchant. The method comprising: initiating a campaign by one or more merchants, accessing transaction data from one or more financial institutions, extracting metadata associated with the transaction data in accordance with at least one rule, analyzing the metadata to identify transaction data associated with the campaign and the one or more merchants, analyzing the transaction data associated with the campaign and the one or more merchants and providing information associated with the campaign to the merchant.