Abstract: Systems and methods for classifying gaps in network activity as normal or anomalous are disclosed. A computer system can identify time gaps between successive network events, which can comprise communications or interactions between entities or devices on a network. The computer system can identify network event data records corresponding to network events that occurred both before and after the identified time gaps. The computer system can use data contained in network event data records corresponding to these network events to derive data features that can be used to train a machine learning to classify time gaps based on those features. After training the machine learning model, the computer system can then extract data features corresponding to unlabeled time gaps, and input those data features into the trained machine learning model in order to classify those time gaps as normal or anomalous.

Abstract: Systems and methods for classifying gaps in network activity as normal or anomalous are disclosed. A computer system can identify time gaps between successive network events, which can comprise communications or interactions between entities or devices on a network. The computer system can identify network event data records corresponding to network events that occurred both before and after the identified time gaps. The computer system can use data contained in network event data records corresponding to these network events to derive data features that can be used to train a machine learning to classify time gaps based on those features. After training the machine learning model, the computer system can then extract data features corresponding to unlabeled time gaps, and input those data features into the trained machine learning model in order to classify those time gaps as normal or anomalous.

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: 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: Described are a method and system for automatically generating a classification of at least one lodging provider and acting thereon. The method includes determining or receiving transaction data representative of a plurality of transactions between the at least one lodging provider and a plurality of financial device holders in a first time period. The method also includes assigning, based at least partially on a predictive model and the transaction data, a classification to the at least one lodging provider, the classification being selected from a plurality of predetermined classifications. The method further includes generating, based at least partially on the assigned classification, identification data of at least one financial device holder of the plurality of financial device holders. The method further includes automatically generating and transmitting at least one communication to the at least one financial device holder.

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