Capital One Services, LLC

Alexandra Colevas

Joshua S Edwards

Michael Bakr Mossoba

Abdelkader M'Hamed Benkreira

Colin Hart

Jason Ji

Kevan Emmott

Christine Berglund

Steven Quadros

Adnan Malik

Matthew Horton

Jared M Alexander

Scott Golder

Allison Abbott

Emma Sagan

Salik Shah

Sophie Bermudez

Sarah Jane Cunningham

Michael Saia

Kaylyn S Gibilterra

Abdelkadar M'Hamed Benkreira

Abdelkadar M'hamed Benkreira

Michael Mossoba

Joshua Edwards

First data indicative of a first plurality of transactions by a user may be processed to generate first behavioral information describing the user. The first behavioral information may be displayed by an interactive user interface. A user input made in response to the first behavioral information may be received and analyzed to generate user preference information indicating a relationship between the first user input and the first behavioral information. Second data indicative of a second plurality of transactions by the user may be received and processed with the user preference information to generate second behavioral information describing the user. The second behavioral information may be displayed by the interactive user interface differently from the first behavioral information by the interactive user interface as a result of the processing of the second data and the user preference information together.

Systems and methods for building dynamic interfaces

A processing platform may receive a plurality of images. The processing platform may determine respective asset types of the plurality of assets based on a computer vision technique. The processing platform may determine respective estimated values of the plurality of assets based on the respective asset types. The processing platform may provide information identifying the respective estimated values of the plurality of assets to two or more recipients. The processing platform may receive allocation information. The processing platform may determine a selected allocation of the plurality of assets for the two or more recipients based on the allocation information and using a second model. The processing platform may perform one or more actions based on the selected allocation.

Computer vision based asset evaluation

Provided, in an aspect, is a system for managing transactions from a financial services computer network. The system has a scalable architecture, and includes a database, one or more processors, a plurality of experience matchers, and a transaction hub. Each experience matcher is associated with a different experience set type, and can receive a transaction list and return an identified experience set. The transaction hub can communicate with multiple experience matchers, and can receive experience sets from the matchers after sending them a list of transactions.

Scalable architecture for managing transactions

A device obtains information concerning a plurality of customer service representatives to identify a customer service representative and a user device associated with the customer service representative. The device selects a test issue of a plurality of test issues to be presented to the customer service representative, and, based on the test issue, a virtual assistant to converse with the customer service representative. The device initiates, based on an availability of the user device, a communication session with the user device, and causes the virtual assistant to converse with the customer service representative regarding the test issue. The device obtains data concerning a performance of the customer service representative during the communication session, processes the data using a machine learning model to determine a performance score for the customer service representative, and causes, based on the performance score for the customer service representative, at least one action to be performed.

Interacting with a user device to provide automated testing of a customer service representative

A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method providing biometric detection of coercion of a user. The system may detect a trigger event associated with a potential transfer of funds and may receive user biometric data. The system may determine, based on stored user biometric data and the detected user biometric data, a confidence level that the stored user biometric data is indicative of biological information representative of a user being in a stressed state. The system may initiate one or more precautionary safety measures.

Systems for detecting biometric response to attempts at coercion

Systems and methods for generating personalized navigation menus are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving account data associated with a plurality of accounts and receiving navigation menu data associated with the accounts. The operations may include training a model based on the account data and the navigation menu data. The operations may include receiving a request associated with a user and receiving user account data associated with the user. The operations may include generating, using the model, a navigation menu based on the request and the user account data. The operations may include providing the navigation menu.

Systems and methods for customized navigation menus

A platform receives transaction information for an entity, where the transaction information identifies a plurality of transactions associated with the entity, and receives entity information associated with the entity. The platform identifies, using a first model, a selected set of transactions based on the transaction information and the entity information, where the first model outputs information identifying the selected set of transactions based on the selected set of transactions being associated with an event, a theme, or a transaction parameter. The platform determines, using a second model, potential modifications to a document based on the selected set of transactions, where the second model receives the information identifying the selected set of transactions or information identifying the event, the theme, or the transaction parameter, and where the second model outputs information identifying the potential modifications. The platform provides the information identifying the potential modifications.

Updating a document based on transaction analysis

A device receives, from a user device, a communication associated with a support issue encountered by a user of the user device and assigns the communication to a position in a support queue based on when the communication is received, wherein the support queue includes data identifying positions of other communications received from other users, and data identifying when the other communications were received. The device processes data identifying the communication and historical communication data describing prior communications associated with the user, with a model, to determine an average time spent on hold by the user for the prior communications. The device modifies the position of the communication in the support queue based on the average time and performs one or more actions based on modifying the position of the communication in the support queue.

Utilizing machine learning with call histories to determine support queue positions for support calls

Provided, in an aspect, is a method for improved visualization of transaction data from a financial services computer network. The method includes receiving, at a computing device, a plurality of experience sets; presenting, on a display of the computing device, a graphical user interface (GUI); and updating the GUI in response to receiving a revision to the plurality of experience sets. Each experience set contains one or more transactions that relate to each other. The GUI includes a scrollable list having at least a portion of the experience set name for experience sets within a selected time period; and a calendar for the time period showing, for each day with at least one experience, a graphical element indicative of a total of all experience set amounts in that day.

Visualization of transaction data

A device receives, from a user device, a communication request that includes information identifying a user of the user device, an organization, and at least one reason for the communication request. The device determines, based on the communication request, information concerning the user, a telephone number associated with the organization, and a menu of an interactive voice response (IVR) system device associated with the telephone number. The device communicates, based on the information concerning the user, the menu of the IVR system device, and the at least one reason for the communication request, with the IVR system device via a communication session initiated based on the telephone number. The device determines, based on communicating with the IVR system device, a time that the user is to interact with the IVR system device via the communication session, and causes the user device to join the communication session at the time.

Interacting with an interactive voice response system device or agent device of an organization

A device causes a communication session to be established between the device and a user device to allow the device and the user device to communicate speech, and receives user speech from the user device. The device processes the user speech using a natural language processing technique to determine a plurality of characteristics of the user speech, and updates a speech generation setting of a plurality of speech generation settings based on the plurality of characteristics of the user speech. The device generates, after updating the speech generation setting, device speech using a text-to-speech technique based on the speech generation setting, and sends the device speech to the user device.

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Arlington, VA, United States of America

Alexandra Colevas