Methods and apparatuses for customized credit card recommendations

A computer-implemented method comprising:

• determining, based at least in part on input from a user via interaction with one or more user interfaces, estimated spend by the user within each of a plurality of categories of goods or services, wherein the estimated spend comprises (1) a first estimated spend for a first category of goods or services and (2) a second estimated spend for a second category of goods or services;
• identifying a plurality of available credit cards and associated reward data;
• generating a score for each credit card of at least a subset of the plurality of credit cards with respect to the user, wherein the score for an individual credit card of the plurality of credit cards is generated by providing a trained model with input data associated with the user, wherein the input data provided to the trained model for the individual credit card includes estimated spend for at least a subset of the plurality of categories of goods or services and reward data for the individual credit card;
• causing presentation, by a user computing device, of a user interface that includes information regarding two or more of the at least a subset of the plurality of credit cards as recommended credit cards for the user based at least in part on the generated scores of the at least a subset of the plurality of credit cards;
• receiving an indication of a user selection of one of the two or more presented credit cards from the user computing device;
• and
• causing presentation, by the user computing device, of content from a credit card server of a credit card institution associated with the selected one of the two or more presented credit cards overlaid on top of the user interface, wherein the content is displayed without redirecting the user computing device to a website of the credit card institution.

A non-transitory computer storage medium storing computer-executable instructions that, when executed by a processor of a computing system, cause the processor to perform operations comprising:

• determining, based at least in part on input from a user via interaction with one or more user interfaces, estimated spend by the user within each of a plurality of categories of goods or services, wherein the estimated spend comprises (1) a first estimated spend for a first category of goods or services and (2) a second estimated spend for a second category of goods or services;
• identifying a plurality of available credit cards and associated reward data;
• generating a score for each credit card of at least a subset of the plurality of credit cards with respect to the user, wherein the score for an individual credit card of the plurality of credit cards is generated by providing a trained model with input data associated with the user, wherein the input data provided to the trained model for the individual credit card includes estimated spend for at least a subset of the plurality of categories of goods or services and reward data for the individual credit card;
• causing presentation, by a user computing device, of a user interface that includes information regarding two or more of the at least a subset of the plurality of credit cards as recommended credit cards for the user based at least in part on the generated scores of the at least a subset of the plurality of credit cards;
• receiving an indication of a user selection of one of the two or more presented credit cards from the user computing device;
• and
• causing presentation, by the user computing device, of content from a credit card server of a credit card institution associated with the selected one of the two or more presented credit cards overlaid on top of the user interface, wherein the content is displayed without redirecting the user computing device to a website of the credit card institution.