Capital One Services, LLC

Noble Ozoka

Matthew Reedy

Andrew Marr

Lin Ward

Allison Fenichel

Varun Gupta

Michael E Snyder

Philip John Spiegel

Kelly Jo Earl Brown

Disclosed embodiments may include a method that includes receiving first identifying information associated with a first user from a computing device; determining a score based on the first identifying information; determining whether the score is less than a threshold; pseudo-randomly generate and transmit a one-time use number to the computing device when the score is less than the threshold. When the score is greater than or equal to the threshold, the method may include transmitting, to the computing device, second instructions prompting the first user to provide second identifying information. Responsive to receiving the second identifying information of the first user, the method may include determining that the second identifying information matches stored second identifying information. Responsive to the second identifying information matching stored second identifying information, the method may include pseudo-randomly generate and transmit the one-time use number to the computing device.

Fraud prevention systems and methods for selectively generating virtual account numbers

A device may receive, from account entity devices, sets of transaction data for transactions between merchants and customers, and may use a first machine learning model to assign the customers to clusters based on measures of similarity among the sets of transaction data. The device may determine travel-related data items in a set of transaction data, of the sets of transaction data, associated with a set of customers assigned to a particular cluster, and may use a second machine learning model to identify a travel experience that has a threshold likelihood of being of interest to the set of customers. The device may receive offers relating to the travel experience, and may provide, to customer devices associated with customers of the set of customers, travel offer packages that include at least one of the offers relating to the travel experience.

Utilizing machine learning models to recommend travel offer packages relating to a travel experience

The disclosed embodiments provide systems and methods for providing real-time warnings to merchants for data breaches. For example, the system may include one or more memory devices storing instructions and one or more processors configured to perform operations consistent with this disclosure. The operations may include collecting and storing transaction authorization requests from one or more merchants. The transaction authorization requests may include a virtual account number associated with an account, the virtual account number being previously bound to a merchant and reusable only for the bound merchant. The operations may further include detecting an event at the bound merchant based on, for example, a transaction authorization request having a mismatch between a transacting merchant and the bound merchant. The operations my further include communicating notification of the event through a communication interface with the bound merchant.

Systems and methods for providing real-time warnings to merchants for data breaches

A behavioral analytics platform may obtain a first data set associated with a first user and a second data set associated with a second user. The behavioral analytics platform may determine a first set of behavioral categories to classify the historical user activity data associated with the first user based on a first set of behavior vectors and determine a second set of behavioral categories to classify the historical user activity data associated with the second user based on a second set of behavior vectors. The behavioral analytics platform may populate one or more user interfaces that are accessible to the first user and/or the second user based on one or more values representing a degree to which the first set of behavioral categories and the second set of behavioral categories correspond to complementary behavioral tendencies, which may be determined using a machine learning technique.

Growing community of inventors

Richmond, VA, United States of America

Noble Ozoka