Abstract: A method for identifying subpopulations may include receiving interaction data associated with interactions from a population of individuals. The interaction data may include a plurality of features. A first subpopulation may be identified based on at least one feature of interaction data of each individual. A second subpopulation may include all individuals other than the first subpopulation. The first subpopulation may be clustered into a first plurality of clusters based on the features. A first subset of features may be determined based on the first clusters. The first subpopulation may be clustered into a second plurality of clusters based on the first subset of features. A range for each feature of a second subset of features may be determined based on the second clusters. A subset of the second subpopulation may be determined based on interaction data for each individual and the range for each feature of the second subset of features.

Abstract: Provided is a method for selectively displaying information regarding activity in a geographic area. The method may include receiving data regarding a plurality of card-present payment transactions that have been conducted in the geographic area during a time interval, determining a plurality of segments of the geographic area, assigning each of the transactions to the segment in which the transaction was conducted, determining aggregate transaction data for each of the segments based on data regarding the transactions that have been assigned to that segment; generating a transaction index for each segment based on a comparison of the aggregate transaction data of that segment with aggregate transaction data of at least one different segment and/or least one predetermined metric, and generating a visual representation comprising data regarding the transaction index of at least one of the segments. A system and computer program product are also provided.

Abstract: Past electronic records may be studied using a computer learning algorithm in order to make predictions of future use of the electronic accounts.

Abstract: A method for identifying subpopulations may include receiving interaction data associated with interactions from a population of individuals. The interaction data may include a plurality of features. A first subpopulation may be identified based on at least one feature of interaction data of each individual. A second subpopulation may include all individuals other than the first subpopulation. The first subpopulation may be clustered into a first plurality of clusters based on the features. A first subset of features may be determined based on the first clusters. The first subpopulation may be clustered into a second plurality of clusters based on the first subset of features. A range for each feature of a second subset of features may be determined based on the second clusters. A subset of the second subpopulation may be determined based on interaction data for each individual and the range for each feature of the second subset of features.

Abstract: Provided is a method for selectively displaying information regarding activity in a geographic area. The method may include receiving data regarding a plurality of card-present payment transactions that have been conducted in the geographic area during a time interval, determining a plurality of segments of the geographic area, assigning each of the transactions to the segment in which the transaction was conducted, determining aggregate transaction data for each of the segments based on data regarding the transactions that have been assigned to that segment; generating a transaction index for each segment based on a comparison of the aggregate transaction data of that segment with aggregate transaction data of at least one different segment and/or least one predetermined metric, and generating a visual representation comprising data regarding the transaction index of at least one of the segments. A system and computer program product are also provided.

Abstract: Past electronic records may be studied using a computer learning algorithm in order to make predictions of future use of the electronic accounts.

Abstract: Past electronic records may be studied using a computer learning algorithm in order to make predictions of future use of the electronic accounts.

Abstract: Provided is a computer-implemented method for determining a merchant category alignment of an account. The method may include comparing at least one parameter associated with transaction data for each user of a plurality of users; segmenting the plurality of users into at least one group of users based on a similarity of the at least one parameter between each user, generating a merchant category similarity matrix for the at least one group of users based on the transaction data for each user in the at least one group, generating an account category matrix based on the transaction data, generating one or more recommendations of an offer associated with the second merchant category, and communicating the one or more recommendations of an offer associated with the second merchant category. A system and computer program product are also disclosed.

Abstract: A method, system, and apparatus for segmenting geographic codes in a behavior-monitored system including a plurality of accounts includes: identifying a plurality of geographic codes associated with the plurality of accounts, wherein each account is associated with a geographic code; receiving geographic transaction metrics associated with each geographic code; determining an optimal number of segments into which the plurality of geographic codes is to be segmented based at least partially on the geographic transaction metrics; segmenting the plurality of geographic codes into the optimal number of segments based at least partially on the geographic transaction metrics, such that each segment is associated with segment transaction metrics; and automatically implementing a target action with respect to at least one account of the plurality of accounts based at least partially on the segment transaction metrics corresponding to at least one segment of the optimal number of segments.

Abstract: Past electronic records may be studied using a computer learning algorithm in order to make predictions of future use of the electronic accounts.