Capital One Services, LLC

Jason Zwierzynski

Joshua S Edwards

Jennifer Kwok

Tania Cruz Morales

Abhay Donthi

Sara Rose Brodsky

Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.

Systems and methods for external account authentication

A method for executing actions based on event data using machine learning is disclosed. The method comprises: receiving occasion data associated with a user; analyzing, using a trained machine learning model, the occasion data to identify an occasion associated with a first classification, wherein the trained machine learning model has been trained based on (i) training occasion data that includes information regarding one or more occasions associated with the training occasion data and (ii) training classification data that includes a prior classification for each of the occasions, to learn relationships between the training occasion data and the training classification data, such that the trained machine learning model is configured to use the learned relationships to identify an occasion associated with a first classification in response to input of the occasion data; determining an action based on the occasion associated with the first classification; and automatically executing the action.

Systems and methods for machine learning based execution of actions based on calendar event data

Systems and methods for detecting and repairing loss of a primary digital communication channel may include a server and a user device. The server may be configured to send a push notification to an application of the user device over a network, receive, in response to the sending of the push notification, push notification status data, apply a predictive model to determine whether the primary digital communication channel has failed based on the push notification and the push notification status data; and transmit, upon a determination that the primary digital communication channel has failed, a communication to the user over one or more alternative digital communication channels.

Systems and methods to determine the loss of ability to notify customer through mobile app and prompt re-download

Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.

Machine learning for determining communication protocols

Systems and methods for confirming and/or updating account information are disclosed. The systems and methods may calculate relocation scores based on transaction data and device location data. The relocation scores may be based on locations of the transactions and/or locations of a user device beyond a registered customer address. A relocation score can be calculated based on a quantity of transactions having transaction locations beyond a first predetermined distance from the customer address as determined by transaction location identifiers. A relocation score can be calculated based on an elapsed duration of time since a customer transaction was completed within the first predetermined distance from the customer address.

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McLean, VA, United States of America

Jason Zwierzynski