Patent ID: 11915313
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a system, cause the system to:
obtain access to an email account associated with an applicant based on the applicant having insufficient domestic historical data for a credit request of the applicant to be processed;
train a plurality of machine learning models to distinguish authentic email accounts from synthetic email accounts,
wherein a first machine learning model, of the plurality of machine learning models, is configured to use an input that includes a set of observations from historical data and generate an output that indicates whether the email account is authentic or synthetic using a target variable based on one or more metrics that relate to patterns associated with email messages included in the email account or patterns in user behaviors that occur during interactions with the email account,
wherein the set of observations include a feature set which is extracted by a second machine learning model, of the plurality of machine learning models, from the historical data by performing natural language processing,
wherein the plurality of machine learning models is trained to recognize patterns in the feature set that lead to a value of the target variable that determines whether the email is authentic or synthetic, and
wherein the patterns in the feature set include one or more of:
 a pattern of received emails,
 a pattern of email header information, a pattern of how email messages marked as spam are handled, or
 a pattern relating to reading behavior;

identify, using the plurality of trained machine learning models, a set of email messages included in the email account that are relevant to the credit request from an email server,
analyze content included in the set of email messages using the plurality of trained machine learning models and the natural language processing to generate non-domestic historical data associated with the applicant;
determine a set of metrics that relate to an estimated creditworthiness of the applicant based on the non-domestic historical data associated with the applicant;
map the set of metrics to a credit score;
obtain information related to income or assets associated with the applicant;
generate a decision that the credit request is granted based on the target variable indicating that emails in the set of email messages are authentic; and
provide information to allow the applicant to open a credit account based on the decision.