Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: An ingress server is operable to perform, through a multi-list evaluator, two different validations: one utilizes a sender network address of a sender's server to determine whether to trust, accept, or reject a connection and one utilizes a domain of a sender email address from an envelope to determine whether to accept or reject a message. The multi-list evaluator may perform the validations in two phases. If a connection can be trusted, the connection is accepted and any message over the connection (in a single session) is accepted and no further validation is necessary. Further, in both phases, the multi-list evaluator can utilize a whitelist maintained by the ingress server to override a blacklist provided by a blacklist supplier. This override can reduce false-positives and drastically reduce delays usually associated with correcting false-positives and improve system throughput.

Abstract: An ingress server is operable to perform, through a multi-list evaluator, two different validations: one utilizes a sender network address of a sender's server to determine whether to trust, accept, or reject a connection and one utilizes a domain of a sender email address from an envelope to determine whether to accept or reject a message. The multi-list evaluator may perform the validations in two phases. If a connection can be trusted, the connection is accepted and any message over the connection (in a single session) is accepted and no further validation is necessary. Further, in both phases, the multi-list evaluator can utilize a whitelist maintained by the ingress server to override a blacklist provided by a blacklist supplier. This override can reduce false-positives and drastically reduce delays usually associated with correcting false-positives and improve system throughput.

Abstract: A computer-implemented method is disclosed. The method includes using reverse geo-coding to determine user transaction locations for a user, determining a number of user transactions for the user that correspond to each of a plurality of statistical area levels, determining a subdivision of each of the plurality of statistical area levels that has the highest number of domestic card present transactions for the user, identifying an effective area of influence (EAI) for the user, based on a determination of a statistical area level that has the highest number of domestic card present transactions for the user, and accessing geographically classified statistics from public data sources related to one or more of the plurality of the statistical area levels. A location based market recommendation is generated based on the geographically classified statistics and the effective area of influence.

Abstract: A computer-implemented method is disclosed. The method includes using reverse geo-coding to determine user transaction locations for a user, determining a number of user transactions for the user that correspond to each of a plurality of statistical area levels, determining a subdivision of each of the plurality of statistical area levels that has the highest number of domestic card present transactions for the user, identifying an effective area of influence (EAI) for the user, based on a determination of a statistical area level that has the highest number of domestic card present transactions for the user, and accessing geographically classified statistics from public data sources related to one or more of the plurality of the statistical area levels. A location based market recommendation is generated based on the geographically classified statistics and the effective area of influence.

Abstract: A first plurality of consumers having a target event is identified. A first dataset and a second dataset for the first plurality of consumers are received. The first dataset comprises first transaction card data for each consumer in the first plurality of consumers for a first time period, where the first time period is prior to the target event. The second dataset comprises second transaction card data for each consumer in the first plurality of consumers during a second time period, where the second time period is after the target event. A first score for each consumer is calculated using the first transaction card data and a second score for each consumer is calculated using the second transaction card data. The first score is compared to the second score for each consumer. In response to a determination that the second score is a threshold lower than the first score, one or more consumers are identified as high dormancy risk.

Abstract: A first plurality of consumers having a target event is identified. A first dataset and a second dataset for the first plurality of consumers are received. The first dataset comprises first transaction card data for each consumer in the first plurality of consumers for a first time period, where the first time period is prior to the target event. The second dataset comprises second transaction card data for each consumer in the first plurality of consumers during a second time period, where the second time period is after the target event. A first score for each consumer is calculated using the first transaction card data and a second score for each consumer is calculated using the second transaction card data. The first score is compared to the second score for each consumer. In response to a determination that the second score is a threshold lower than the first score, one or more consumers are identified as high dormancy risk.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: An ingress server is operable to perform, through a multi-list evaluator, two different validations: one utilizes a sender network address of a sender's server to determine whether to trust, accept, or reject a connection and one utilizes a domain of a sender email address from an envelope to determine whether to accept or reject a message. The multi-list evaluator may perform the validations in two phases. If a connection can be trusted, the connection is accepted and any message over the connection (in a single session) is accepted and no further validation is necessary. Further, in the both phases, the multi-list evaluator can utilize a whitelist maintained by the ingress server to override a blacklist provided by a blacklist supplier. This override can reduce false-positives and drastically reduce delays usually associated with correcting false-positives and improve system throughput.

Abstract: Provided is a computer-implemented method for predicting payment transactions using a machine learning technique that includes receiving transaction data, generating a categorical transaction model based on the transaction data, determining a plurality of prediction scores including determining, for one or more users, a prediction score in each merchant category of a plurality of merchant categories for each predetermined time segment of a plurality of predetermined time segments, where a respective prediction score includes a prediction of whether a user will conduct a payment transaction in a merchant category at a time associated with a predetermined time segment associated with the respective prediction score, determining a recommended merchant category and a recommended predetermined time segment of at least one offer, generating the at least one offer, and communicating the at least one offer to the one or more users. A system and computer program product are also disclosed.

Abstract: Systems and methods are provided for processing messages for registered users within an allowed domain. The method includes performing domain-registration and auto-registration for non-registered users. The method allows users to utilize messaging services without having to individually register each and every user upfront.

Abstract: Systems and methods are provided for processing messages for registered users within an allowed domain. The method includes performing domain-registration and auto-registration for non-registered users. The method allows users to utilize messaging services without having to individually register each and every user upfront.