Credit scoring and pre-approval engine integration

A credit scoring and pre-approval system comprising:

• a server comprising a processor and a memory, wherein the memory contains a credit score tool and a credit pre-approval tool;
• and
• a database containing user information, prior pre-approval offer determinations made by the credit pre-approval tool, and application usage data, the database in data communication with the processor,
• wherein, upon receipt of a credit score request from a client device, the processor is configured to:
• execute the credit score tool to obtain a credit score associated with a user and associated credit score information, and
• execute the credit pre-approval tool to apply a predictive model to the received credit score, the associated credit score information, prior pre-approval offer determinations, and application usage data to determine whether to transmit a pre-approved offer of credit to the client device, the application usage data comprises data relating to the user's interaction with the credit score tool, including the device used by the user to access the credit score tool and the time spent by the user interacting with the credit score tool, wherein:
• the predictive model includes a target variable and a set of predictor variables, the set of predictor variables including at least the application usage data, and
• the predictive learning model is updated upon changes to the database through the use of at least one machine learning algorithm selected from the group of gradient boosting machine, logistic regression, and neural networks.

A method for credit scoring and pre-approval, comprising:

• establishing, by a processor, data communication with one or more databases containing a credit score, associated credit score information, one or more prior pre-approval determinations, and application usage data relating to one or more interactions with a credit score tool, wherein the application usage data includes an identification of a first client device used to access the credit score tool and the time spent by the first client device interacting with the credit score tool;
• applying, by the processor, a predictive model to the credit score, the associated credit score information, the prior pre-approval offer determinations, and the application usage data, the predictive model including a target variable and a plurality of predictor variables, wherein the target variable is whether an application was submitted for a pre-approved offer of credit and the plurality of predictor variables includes the credit score, the associated credit score information, the prior pre-approval offer determinations, and the application usage data;
• and
• determining, by the processor and based on the predictive model, whether to transmit a pre-approved offer of credit to at least one selected from the group of the first client device and a second client device;
• and
• updating, by the processor, the predictive model through the use of at least one machine learning algorithm selected from the group of gradient boosting machine, logistic regression, and neural networks upon a change to one or more of the credit score, the associated credit score information, the prior pre-approval offer determinations, and the application usage data.