System, Method, and Computer Program Product for Implementing a Generative Adversarial Network to Determine Activations

A system for generating a machine learning model to classify an account based on merchant activation, comprising:

• at least one processor programmed or configured to:
• provide, during a training procedure, an input to a generator network of a generative adversarial network (GAN), wherein the generator network comprises:
• a first dense layer comprising a rectified linear unit (ReLu) function with 16 nodes;
• a second dense layer comprising a ReLu function with 32 nodes, wherein the second dense layer is fully connected to the first dense layer;
• a third dense layer comprising a ReLu function with 64 nodes, wherein the third dense layer is fully connected to the second dense layer;
• a fourth dense layer comprising a ReLu function with 128 nodes, wherein the fourth dense layer is fully connected to the third dense layer;
• and
• a fifth dense layer comprising a ReLu function with 182 nodes, wherein the fifth dense layer is fully connected to the fourth dense layer;
• generate an output of the generator network based on the input, wherein the output comprises a generated dataset, wherein the generated dataset comprises a first plurality of sets of values for each of a plurality of features and the first plurality of sets of values for each of the plurality of features comprises data associated with a first plurality of payment transactions conducted using a plurality of accounts and involving a plurality of merchants;
• provide, during the training procedure, the output of the generator network as an input to a discriminator network of the GAN;
• provide, during the training procedure, a training dataset as an input to the discriminator network of the GAN, wherein the training dataset comprises a second plurality of sets of values for each of the plurality of features and the second plurality of sets of values for each of the plurality of features comprises data associated with a second plurality of payment transactions conducted using the plurality of accounts and involving the plurality of merchants;
• update, during the training procedure, the generator network of the GAN based on a first output of the discriminator network of the GAN 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;
• and
• update, during the training procedure, the discriminator network of the GAN based on a second output of the discriminator network of the GAN having a label that indicates whether a selected account of the plurality of accounts is going to conduct a first payment transaction with a selected merchant of the plurality of merchants.

A computer program product for generating a machine learning model to classify an account based on merchant activation comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:

• provide, during a training procedure, an input to a generator network of a generative adversarial network (GAN), wherein the generator network comprises:
• a first dense layer comprising a rectified linear unit (ReLu) function with 16 nodes;
• a second dense layer comprising a ReLu function with 32 nodes, wherein the second dense layer is fully connected to the first dense layer;
• a third dense layer comprising a ReLu function with 64 nodes, wherein the third dense layer is fully connected to the second dense layer;
• a fourth dense layer comprising a ReLu function with 128 nodes, wherein the fourth dense layer is fully connected to the third dense layer;
• and
• a fifth dense layer comprising a ReLu function with 182 nodes, wherein the fifth dense layer is fully connected to the fourth dense layer;
• during the training procedure, implement a dropout of 30% at each of the first dense layer, the second dense layer, the third dense layer, the fourth dense layer, and the fifth dense layer of the generator network of the GAN;
• and
• during the training procedure, implement a batch normalization process at each of the first dense layer, the second dense layer, the third dense layer, the fourth dense layer, and the fifth dense layer of the generator network of the GAN;
• generate an output of the generator network based on the input, wherein the output comprises a generated dataset, wherein the generated dataset comprises a first plurality of sets of values for each of a plurality of features and the first plurality of sets of values for each of the plurality of features comprises data associated with a first plurality of payment transactions conducted using a plurality of accounts and involving a plurality of merchants;
• provide, during the training procedure, the output of the generator network as an input to a discriminator network of the GAN;
• provide, during the training procedure, a training dataset as an input to the discriminator network of the GAN, wherein the training dataset comprises a second plurality of sets of values for each of the plurality of features and the second plurality of sets of values for each of the plurality of features comprises data associated with a second plurality of payment transactions conducted using the plurality of accounts and involving the plurality of merchants;
• update, during the training procedure, the generator network of the GAN based on a first output of the discriminator network of the GAN having a label that indicates whether a set of values of each of the plurality of features is real set of values or a fake set of values;
• and
• update, during the training procedure, the discriminator network of the GAN based on a second output of the discriminator network of the GAN having a label that indicates whether a selected account of the plurality of accounts is going to conduct a first payment transaction with a selected merchant of the plurality of merchants.