System, Method, and Computer Program Product for Implementing a Hybrid Deep Neural Network Model to Determine a Market Strategy

A system for implementing a hybrid deep neural network model to determine a market strategy, comprising:

• at least one processor programmed or configured to:
• generate 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;
• generate a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions;
• generate a combined model, wherein the combined model comprises a combination of the first model, the second model, and feature data associated with one or more features, wherein generating the combined model comprises concatenating the first model, the second model, and the feature data;
• and
• determine 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 computer program product for implementing a hybrid deep neural network model to determine a market strategy, the computer program product 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:

• generate 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;
• generate a second model comprising a deep neural network model, the deep neural network model comprising a plurality of hidden layers, wherein the plurality of hidden layers of the second model comprise a first hidden layer configured to provide an output having a first amount of dimensions to a second hidden layer, the second hidden layer configured to provide an output having a second amount of dimensions less than the first amount of dimensions;
• and
• generate a combined model, wherein the combined model comprises a combination of the first model, the second model, and feature data associated with one or more features, wherein generating the combined model comprises concatenating the first model, the second model, and the feature data.