Abstract: Provided is a method for matching card transaction data to mobile application data. The method may include generating a plurality of regions within a geographic area, each region of the plurality of regions associated with a region identifier. Transaction data associated with an account identifier and mobile application data associated a user identifier may be received. A region for each transaction and for each interaction may be determined based on the transaction and interaction locations. A transaction signature may be generated for each account and an interaction signature may be generated for each user identifier. At least one transaction may be matched to at least one interaction signature. At least one account identifier may be linked to at least one user identifier based on matching the at least one transaction signature to the at least one interaction signature. A system and computer program product are also disclosed.

Abstract: Provided is a method for matching card transaction data to mobile application data. The method may include generating a plurality of regions within a geographic area, each region of the plurality of regions associated with a region identifier. Transaction data associated with an account identifier and mobile application data associated a user identifier may be received. A region for each transaction and for each interaction may be determined based on the transaction and interaction locations. A transaction signature may be generated for each account and an interaction signature may be generated for each user identifier. At least one transaction may be matched to at least one interaction signature. At least one account identifier may be linked to at least one user identifier based on matching the at least one transaction signature to the at least one interaction signature. A system and computer program product are also disclosed.

Abstract: Provided are methods for iteratively refining a training data set which may include training a first predictive model based on a first set of user profiles; determining a classification for each user profile of a second set of user profiles; determining a performance score for the first predictive model; determining to update the first predictive model based on the performance score for the first predictive model; determining a classification for each user profile of the first set of user profiles using the first predictive model; and selecting at least one user profile of the first set of user profiles to include in a removal set of user profiles. In some non-limiting embodiments or aspects, the method may include removing each user profile included in the removal set of user profiles from the first set of user profiles. Systems and computer program products are also provided.

Abstract: Provided are methods for iteratively refining a training data set which may include training a first predictive model based on a first set of user profiles; determining a classification for each user profile of a second set of user profiles; determining a performance score for the first predictive model; determining to update the first predictive model based on the performance score for the first predictive model; determining a classification for each user profile of the first set of user profiles using the first predictive model; and selecting at least one user profile of the first set of user profiles to include in a removal set of user profiles. In some non-limiting embodiments or aspects, the method may include removing each user profile included in the removal set of user profiles from the first set of user profiles. Systems and computer program products are also provided.

Abstract: Provided is a system, method, and computer program product for adjusting a model. The method includes training or receiving a model based on transaction data for a plurality of account holders, the model configured to classify each individual of the plurality of account holders, segmenting each individual of at least a portion of account holders of the plurality of account holders into at least one subgroup of a plurality of subgroups, separately executing the model for each subgroup of the plurality of subgroups by inputting, into the model, a portion of the transaction data associated with individuals in each subgroup, comparing a plurality of outputs of the model resulting from the separate execution of the model for each subgroup of the plurality of subgroups, and adjusting the model based on at least one differential between the plurality of outputs.

Abstract: Provided are methods for iteratively refining a training data set which may include training a first predictive model based on a first set of user profiles; determining a classification for each user profile of a second set of user profiles; determining a performance score for the first predictive model; determining to update the first predictive model based on the performance score for the first predictive model; determining a classification for each user profile of the first set of user profiles using the first predictive model; and selecting at least one user profile of the first set of user profiles to include in a removal set of user profiles. In some non-limiting embodiments or aspects, the method may include removing each user profile included in the removal set of user profiles from the first set of user profiles. Systems and computer program products are also provided.

Abstract: Provided are methods for iteratively refining a training data set which may include training a first predictive model based on a first set of user profiles; determining a classification for each user profile of a second set of user profiles; determining a performance score for the first predictive model; determining to update the first predictive model based on the performance score for the first predictive model; determining a classification for each user profile of the first set of user profiles using the first predictive model; and selecting at least one user profile of the first set of user profiles to include in a removal set of user profiles. In some non-limiting embodiments or aspects, the method may include removing each user profile included in the removal set of user profiles from the first set of user profiles. Systems and computer program products are also provided.

Abstract: Provided is a method for matching card transaction data to mobile application data. The method may include generating a plurality of regions within a geographic area, each region of the plurality of regions associated with a region identifier. Transaction data associated with an account identifier and mobile application data associated a user identifier may be received. A region for each transaction and for each interaction may be determined based on the transaction and interaction locations. A transaction signature may be generated for each account and an interaction signature may be generated for each user identifier. At least one transaction may be matched to at least one interaction signature. At least one account identifier may be linked to at least one user identifier based on matching the at least one transaction signature to the at least one interaction signature. A system and computer program product are also disclosed.

Abstract: Provided are methods for iteratively refining a training data set which may include training a first predictive model based on a first set of user profiles; determining a classification for each user profile of a second set of user profiles; determining a performance score for the first predictive model; determining to update the first predictive model based on the performance score for the first predictive model; determining a classification for each user profile of the first set of user profiles using the first predictive model; and selecting at least one user profile of the first set of user profiles to include in a removal set of user profiles. In some non-limiting embodiments or aspects, the method may include removing each user profile included in the removal set of user profiles from the first set of user profiles. Systems and computer program products are also provided.