Abstract: A system is configured to retrieve historical raw transaction data, wherein each transaction is one of a target or non-target transaction. The target transactions are related to a target transaction event. A target transaction identifier is appended to each target transaction. The raw transaction data is stored to a first data table. A first neural network is trained using the first data table to generate a training data classification model. The training data classification model is applied to the first data table. A first similarity score distribution associated with the target transactions and a second similarity score distribution associated with the non-target transactions is determined. A plurality of non-target transactions whose combined similarity score distribution matches the target transactions is selected. The target transactions and the selected plurality of non-target transactions are stored to a second data table and a second neural network is trained using the second data table.

Abstract: A system is configured to retrieve a set of customer raw transaction data, wherein the transactions are devoid of any target transactions of interest. An impact neural network model is applied to the transaction data using a “notTarget” variable. The “notTarget” variable indicates that the target transaction of interest is not included in the transaction data. The model predicts a first result based on the “notTarget” variable. The model is applied to the transaction data using an “isTarget” variable. The “isTarget” variable indicates that the target transaction of interest is included in the set of customer raw transaction data. The model predicts a second result based on the “isTarget” variable. The system determines a difference between the second and first results. The difference is a predicted incremental impact on cardholder behavior. The system presents the predicted incremental impact on cardholder behavior to an issuer associated with the transaction data.

Abstract: Disclosed method comprises receiving from a user interface, a request to generate an optimized space dataset; generating a first instruction configured to receive a first dataset comprising at least one of a space allocated to the product, product attribute, and product performance value corresponding to a plurality of institutions; automatically calculating an elasticity range based on the space allocated to the product; calculating an unbounded asymptote and elasticity values; upon the unbounded elasticity value being outside the elasticity range, dynamically adjusting the elasticity value based on the elasticity range; iteratively calculating a bounded asymptote value based on the second dataset data points until the asymptote value is within the adjusted elasticity range; and generating a graphical representation to illustrate an optimized space trend comprising plurality of data points.

Abstract: Disclosed method comprises receiving from a user interface, a request to generate an optimized space dataset; generating a first instruction configured to receive a first dataset comprising at least one of a space allocated to the product, product attribute, and product performance value corresponding to a plurality of institutions; automatically calculating an elasticity range based on the space allocated to the product; calculating an unbounded asymptote and elasticity values; upon the unbounded elasticity value being outside the elasticity range, dynamically adjusting the elasticity value based on the elasticity range; iteratively calculating a bounded asymptote value based on the second dataset data points until the asymptote value is within the adjusted elasticity range; and generating a graphical representation to illustrate an optimized space trend comprising plurality of data points.