Capital One Services, LLC

Francisco Perez Leon

Mohammad Shami

Fernando San Martin Jorquera

Koon Heng Ivan Teo

Yoonseong Kim

Yazdan Shirvany

Volodymyr Orlov

Qingyi Sui

Systems and methods for customizing business applications based upon user intent scores is described. A machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with the business application may be trained using data regarding prior interactions between a business application and a plurality of users. The machine learning model may thereafter provide a probability score for a particular user, the probability score indicating the likelihood that the user will engage in the specific activity for which the model has been trained to predict. The probability may be combined with a business value factor to produce a user intent score indicating the relative value of the user engaging in the specific activity. A business application comprising an app executing on a client device, a webpage, an automated menu at a call-in service center, or human operator interacting with the user at a call in the service center may be customized for the particular user based upon the user intent scores.

Machine learning-driven servicing interface

Disclosed are systems and methods for monitoring user-defined metrics. A method may include: receiving, from a user device, a metric definition usable to generate queries to obtain data for a metric to be monitored; receiving, from the user device, a monitoring configuration indicative of a manner in which a metric monitoring process associated with the metric definition is to be repeatedly performed; storing the metric definition in a metric definition database; and repeatedly performing the metric monitoring process in accordance with the monitoring configuration. The metric monitoring process may include: retrieving the metric definition from the metric definition database; generating a database query based on the metric definition, the database query including one or more executable database statements defined by the metric definition; executing the database query to obtain query result data, the query result data being data for the metric; and storing the query result data.

Systems and methods for monitoring user-defined metrics

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Richmond, CA, United States of America

Francisco Perez Leon