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 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 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 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 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.