Abstract: During operation of a system, a financial transaction of an individual is associated with one or more predefined categories based on scores that indicate the likelihood of association. For example, a given predefined category may include a merchant name (such as the name of a potential counterparty in the financial transaction) and/or an attribute associated with one or more merchants. The score for a given predefined category may be determined based on a number of occurrences of the given predefined category in financial-transaction histories of a subset of a group of individuals who have common spending patterns in their financial-transaction histories. Moreover, the spending pattern may be based on values of financial transactions in the financial-transaction histories.

Abstract: During operation of a system, a financial transaction of an individual is associated with one or more predefined categories based on scores that indicate the likelihood of association. For example, a given predefined category may include a merchant name (such as the name of a potential counterparty in the financial transaction) and/or an attribute associated with one or more merchants. The score for a given predefined category may be determined based on a number of occurrences of the given predefined category in financial-transaction histories of a subset of a group of individuals who have common spending patterns in their financial-transaction histories. Moreover, the spending pattern may be based on values of financial transactions in the financial-transaction histories.

Abstract: During operation of a system, a financial transaction of an individual is associated with one or more predefined categories based on scores that indicate the likelihood of association. For example, a given predefined category may include a merchant name (such as the name of a potential counterparty in the financial transaction) and/or an attribute associated with one or more merchants. The score for a given predefined category may be determined based on financial-transaction preferences of the individual, where the financial-transaction preferences are specified by financial transactions in a financial-transaction history of the individual. The financial-transaction preferences may include a business-size preference (such as local versus national chains) and/or predefined categories in the financial-transaction history.

Abstract: During this autocomplete technique, autocomplete results for data-entry information from a user are ranked based on financial-transaction histories of a group of entities and the user, where the group of entities and the user belong to a common business cohort. In particular, the business cohort may include entities that: are located proximate to the user, have a similar size as a business associated with the user (such as a similar number of employees and/or similar revenue), and/or occur frequently in a financial-transaction history of the user (and don't occur frequently in the financial-transaction histories of the group of entities). The ranking may be used to increase the accuracy or relevance of the autocomplete results to the user. For example, the ranking may give preference in the autocomplete results to entities in the group of entities (relative to other entities, such as those in different business cohorts).

Abstract: The disclosed embodiments provide a system that determines a likelihood that a first entity and a second entity are the same entity. During operation, the system obtains financial data related to the first entity and obtains financial data related to the second entity. Next, the system determines the likelihood that the first entity and the second entity are the same entity based on the relationship between the financial data for the first entity and the financial data for the second entity. Then, the system alters a record related to the first entity or the second entity based on the likelihood.

Abstract: During operation of a system, a financial transaction of an individual is associated with one or more predefined categories based on scores that indicate the likelihood of association. For example, a given predefined category may include a merchant name (such as the name of a potential counterparty in the financial transaction) and/or an attribute associated with one or more merchants. The score for a given predefined category may be determined based on financial-transaction preferences of the individual, where the financial-transaction preferences are specified by financial transactions in a financial-transaction history of the individual. The financial-transaction preferences may include a business-size preference (such as local versus national chains) and/or predefined categories in the financial-transaction history.

Abstract: Some embodiments provide a system that facilitates use of an auto-completion system. During operation, the system receives an input text string entered by a user. The system determines a set of text strings, wherein each of the text strings in the set can replace the input text string entered by the user. Next, the system calculates a commercial intent score for each text string in the set. Following this, the system selects a replacement text string from the set of text strings based on commercial intent scores computed for each text string in the set. Finally, the system displays the selected replacement text string in the place of the input text string to the user.

Abstract: During this autocomplete technique, autocomplete results for data-entry information from a user are ranked based on financial-transaction histories of a group of entities and the user, where the group of entities and the user belong to a common business cohort. In particular, the business cohort may include entities that: are located proximate to the user, have a similar size as a business associated with the user (such as a similar number of employees and/or similar revenue), and/or occur frequently in a financial-transaction history of the user (and don't occur frequently in the financial-transaction histories of the group of entities). The ranking may be used to increase the accuracy or relevance of the autocomplete results to the user. For example, the ranking may give preference in the autocomplete results to entities in the group of entities (relative to other entities, such as those in different business cohorts).

Abstract: Systems and methods provide a mechanism to create and maintain web pages and other types of output pages. The system and methods use an author markup language (AML) to define output pages. Additionally, a Component Markup Language (CML) may be used to define components that may be referenced by the AML. The AML and CML may be translated to an intermediate format, which may then be further processed at runtime to combine dynamic data with the intermediate format to produce the output page.