Capital One Services, LLC

Abhay Donthi

Jennifer Kwok

Joshua S Edwards

Tania Cruz Morales

Jason Zwierzynski

Sara Rose Brodsky

Salik Shah

Dwij Trivedi

Leeyat Bracha Tessler

Michael Bakr Mossoba

Lukiih Cuan

Phoebe Atkins

Asher Smith-Rose

Michael Wolf

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

Disclosed embodiments pertain to provisioning a virtual payment card for an authorized user in a browser. A primary cardholder can request the addition of an individual as an authorized user on the primary cardholder's account. Further information regarding the individual can be solicited and utilized to compute a risk score associated with adding the individual as an authorized user. Further processing of the request, including whether to terminate or continue, can be based on comparing the risk score to a predetermined threshold. If processing is continued, a virtual payment card linked to the primary cardholder account is generated. Next, the virtual card can be added as a payment method to a browser profile of the individual on a network-accessible computing resource. Subsequently, the authorized user can access the virtual card to make online shopping payments with a web browser personalized with the browser profile.

Browser provisioned virtual payment card for an authorized user

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a notification to engage a transmission guard bot in a communication session; generating a trained transmission guard bot configured with communication capabilities to engage the communication session; engaging the trained transmission guard bot into the communication session between the plurality of computing devices associated with the plurality of users; utilizing the trained transmission guard bot to continually monitor a plurality of content data associated with the communication session for a predetermined period of time; detecting a suspicious content data point within the plurality of content data associated with the communication session based on the continual monitoring of the at least one transmission guard bot; determining a risk assessment metric associated with the communication session; and invoking an action related to spam prevention via the at least one trained transmission guard bot.

Computer-based systems configured to dynamically enhance a spam prevention functionality on a computing device and methods of use thereof

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

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a permission indicator identifying a permission by the first user to detect calls being received by the computing device; receiving an indication of a particular call being received from a second user at a particular phone number; detecting a triggering condition to verify when the particular phone number is associated with the particular entity; instructing the computing device to execute a computer routine to verify when the particular phone number is associated with the particular entity; receiving an indication from the computer routine when the particular phone number is not associated with the particular entity; instructing the computing device to perform at least one security action; and instructing to notify a second computing device associated with the particular entity with information pertaining to the particular call.

Computer-based systems configured for automatic detection of a verified identity related to interaction sessions and methods of use thereof

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

Systems and methods of provisioning post-interaction values are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving an interaction session message from a computing terminal, the interaction session message comprising an identifying data of a personal transacting device; defining, based on an interaction session: i) a user-specific context parameter about the user, and ii) a location-specific context parameter about a physical location of the computing terminal; defining post-interaction values based on the user-specific context parameter and the location-specific context parameter; sending the post-interaction values to the computing terminal; instructing the computing terminal to transmit the post-interaction values to the circuitry of the personal transacting device; and sending an instruction to a computing device associated with the user to establish a second session between the personal transacting device and the computing device and transfer the post-interaction values to the computing device.

Computer-based systems configured for post-interaction data provisioning and methods of use thereof

Disclosed aspects and embodiments pertain to customer distress assistance. A call can be detected to a callee from a caller. The call can be analyzed and determined to be a fraud or spam risk. In response, callee monitoring is triggered. The monitoring can capture biometrics or speech of the callee. Distress in the callee can be determined through analysis of the biometrics or speech and comparison to reference biometrics or speech of the callee. An individual associated with the callee can be selected based on determining the distress in the customer. The individual can be contacted through a notification or a call to assist the callee in distress.

Customer distress assistance

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

A computer-implemented method for authenticating a source of a telephone call to a recipient of the telephone call may include receiving, prior to receiving the telephone call, a request to input first authentication information. The first authentication information may include a passcode or a selection of a security question. The method may include receiving input of the first authentication information and sending, by the user device, the first authentication information to a computing device associated with the source of the telephone call. The method may include receiving the telephone call from the source and receiving input from the recipient of the telephone call to answer the telephone call. The method may include receiving second authentication information from the computing device, where the second authentication information may include the passcode or an answer to the security question. The method may include outputting the second authentication information.

Systems and methods for detecting and/or preventing vishing attacks using user-generated authentication information

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

Abhay Donthi