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 computer-implemented method for implementing a hybrid deep neural network. The method may include generating a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating a combined model; and determining a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data. A system and computer program product 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.

Abstract: Provided is a computer-implemented method for implementing a hybrid deep neural network. The method may include generating a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating a combined model; and determining a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data. A system and computer program product 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 for developing a geographic agnostic machine learning model. The system may select transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, normalize the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, generate a machine learning model using the training data, and determine a classification of an input using the machine learning model. A method and computer program product are also disclosed.

Abstract: Provided is a computer-implemented method for generating a machine learning model to classify an account based on merchant activation, including providing an input to a generator network of a generative adversarial network (GAN) to generate an output; providing the output as input to a discriminator network; providing a training dataset as input to the discriminator network; and updating the generator network based on a first output of the discriminator network having a label that indicates whether a set of values of each of the plurality of features is a real set of values or a fake set of values. The method may include updating the discriminator network based on a second output of the discriminator network having a label that indicates whether a selected account of the plurality of accounts is going to conduct a first payment transaction. A system and computer program product are also provided.

Abstract: Provided is a computer-implemented method for generating a machine learning model to classify an account based on merchant activation, including providing an input to a generator network of a generative adversarial network (GAN) to generate an output; providing the output as input to a discriminator network; providing a training dataset as input to the discriminator network; and updating the generator network based on a first output of the discriminator network having a label that indicates whether a set of values of each of the plurality of features is a real set of values or a fake set of values. The method may include updating the discriminator network based on a second output of the discriminator network having a label that indicates whether a selected account of the plurality of accounts is going to conduct a first payment transaction. A system and computer program product are also provided.

Abstract: Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. 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 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 is a computer-implemented method for generating a machine learning model to classify an account based on merchant activation, including providing an input to a generator network of a generative adversarial network (GAN) to generate an output; providing the output as input to a discriminator network; providing a training dataset as input to the discriminator network; and updating the generator network based on a first output of the discriminator network having a label that indicates whether a set of values of each of the plurality of features is a real set of values or a fake set of values. The method may include updating the discriminator network based on a second output of the discriminator network having a label that indicates whether a selected account of the plurality of accounts is going to conduct a first payment transaction. A system and computer program product are also provided.

Abstract: Provided is a computer-implemented method for determining a customer dormancy profile including receiving transaction data associated with a plurality of payment transactions conducted using an account of an account holder, generating an output of a first residual processing block based on the transaction data associated with the plurality of payment transactions, providing the output of the first residual processing block to a concatenate function block and to a second residual processing block, generating an output of the second residual processing block based on the output of the first residual processing block, generating an output of the concatenate function block based on the output of the first residual processing block and the output of the second residual processing block, and determining an account dormancy label based on the output of the concatenate function block. A system and computer program product are also provided.

Abstract: Provided is a computer-implemented method for implementing a hybrid deep neural network. The method may include generating a first model comprising a generalized matrix factorization model, the generalized matrix factorization model configured to determine one or more latent factors based on receiving transaction data associated with one or more payment transactions; generating a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers; generating a combined model; and determining a rating for a payment account based on transaction data associated with a plurality of payment transactions, wherein the rating comprises an indication that the payment account will be used to conduct a plurality of payment transactions involving a merchant, and wherein the transaction data comprises merchant transaction data and user transaction data. A system and computer program product are also provided.

Abstract: Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. A system and computer program product are also disclosed.