Abstract: Financial data associated with one or more “contributing consumers” is obtained from one or more sources. The financial data obtained from the one or more contributing consumers is then aggregated, analyzed, and/or categorized, according to one or more events causing the changes and one or more criteria/parameters associated with the financial transaction data and/or the contributing consumer. The aggregated and/or categorized data representing the event related changes in the financial data is then stored. A “user consumer” then initiates a request for predictive event cost data associated with one or more specified events and the aggregated and/or categorized data representing the event related changes in the financial data is searched based. Results data representing the changes in the financial data for one or more similarly situated contributing consumers associated the specified event is then obtained and presented to the user consumer.

Abstract: Financial data associated with one or more “contributing consumers” is obtained from one or more sources and categorized and associated with a specific expense/income category. One or more attributes associated with the contributing consumers are then identified and used to analyze, aggregate, and categorize the financial data according to the attributes. Data representing a user financial transaction is then obtained for categorization and one or more specific user attributes associated with the user are identified. The user financial transaction is then categorized based, at least in part, on the categorization of similar contributing consumer financial transactions by contributing consumers having at least one of the specific user attributes.

Abstract: A method for categorizing financial transactions, that includes identifying a vendor of the first financial transaction. An attempt is made to categorize the transaction on a first categorization level complying with a first categorization scheme, then a second categorization level that complies with a second categorization scheme, and then a third categorization level that complies with a third categorization scheme. The first categorization scheme differs from the second categorization scheme. When the transaction cannot be categorized on a previous categorization level, the attempt is made for the next level and the second categorization level. The transaction is categorized on the third level. The method further includes displaying the vendor category.

Abstract: A system and method are provided for automated transaction splitting. A central repository gathers details of multi-category transactions between a first payee (e.g., merchant, investment firm) and one or more users. Each category's proportionate share of the total of the transactions is calculated and used to configure an average or model transaction. When a new transaction is received, it is automatically split among the categories based on the model transaction. Multiple types or tiers of model transactions may be applied. For example, a user-specific model transaction may reflect one specific user's history of transactions with the payee. A community model transaction may reflect multiple users' transactions. Similarly, one type of model transaction may reflect transactions with a single payee, while another type may reflect transactions with related payees (e.g., payees having similar businesses).

Abstract: A system that automatically classifies a financial transaction as a recurring financial transaction based on classification data obtained from a set of users is presented. The system can also determine the frequency at which the recurring financial transaction repeats. If the financial transaction is classified as a recurring financial transaction, the system can mark the financial transaction as a recurring financial transaction. Alternatively, the system can notify the user that the classification data indicates that the financial transaction can be classified as a recurring financial transaction.

Abstract: A system and method are provided for automated transaction splitting. A central repository gathers details of multi-category transactions between a first payee (e.g., merchant, investment firm) and one or more users. Each category's proportionate share of the total of the transactions is calculated and used to configure an average or model transaction. When a new transaction is received, it is automatically split among the categories based on the model transaction. Multiple types or tiers of model transactions may be applied. For example, a user-specific model transaction may reflect one specific user's history of transactions with the payee. A community model transaction may reflect multiple users' transactions. Similarly, one type of model transaction may reflect transactions with a single payee, while another type may reflect transactions with related payees (e.g., payees having similar businesses).

Abstract: A system that determines a standard payee name for a payee is presented. During operation, the system receives a non-standard payee name, wherein the non-standard payee name can be a user-defined payee name, which is defined by a user, or a normalized payee name, which is defined by a financial institution. Next, the system determines whether the non-standard payee name is associated with the standard payee name by using the non-standard payee name to search through data structures containing associations between one or more of the following: user-defined payee names and normalized payee names; user-defined payee names and standard payee names; and normalized payee names and standard payee names. If the non-standard payee name is associated with a standard payee name, the system can perform specified actions.

Abstract: A method for categorization of transactions, the method including receiving a request for a transaction from a user of a plurality of users, determining an availability of an object association for an object name of an object of the transaction, wherein the object association is a preferred object association of the user, selecting a collaborative object association for the object name when the preferred object association of the user is not available, wherein the collaborative object association is selected based on the preferred object association of the plurality of users for the object name, and providing the collaborative object association to the user.