Capital One Services, LLC

Muralidharan Balasubramanian

Julie Dallen

Eric K Barnum

David Watson

Vijay Sahebgouda Bantanur

Methods, systems, devices, and computer-readable media for detecting multi-party events and transactions are provided. User data may be monitored to detect data associated with an event involving multiple individuals, such as by identifying transaction data associated with certain types of merchants and/or scheduling, calendar, or correspondence data indicative of an event. The data may be further analyzed to identify a date, location, and/or parties associated with the event. A multi-party event may be generated. The user data may continue to be monitored to identify transactions associated with multiple parties and occurring during a time and/or at a location of the event. At a conclusion of the event, the transactions may be aggregated and an optimal payment scheme may be determined for settlement of the transactions between the parties. In accordance with the determined payment scheme, delegation of portions of the aggregated transactions may be initiated for settlement amongst the parties.

Automated multi-party event and transaction decisioning system

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.

Intelligent services and training agent for application dependency discovery, reporting, and management tool

Intelligent services for application dependency discovery, reporting, and management tool

Testing agent for application dependency discovery, reporting, and management tool

Dependency analyzer in application dependency discovery, reporting, and management tool

Methods, systems, devices, and computer-readable media for a multi-party transaction decisioning system are provided. The system may analyze a purchase transaction associated with a first user and may identify that the transaction is associated with more than one party based on determining that an amount of the transaction is atypical for the first user. The system may identify a party associated with the multi-party transaction based on analyzing data associated with the first user. The system may generate a delegation request for a portion of the amount of the multi-party transaction to be delegated to the identified party and may transmit the delegation request to the identified party. Subsequently, the system may receive a payment transaction from the identified party, and the system may identify that the payment transaction is associated with the previously transmitted delegation request.

Automated multi-party transaction decisioning system

Discovery crawler for application dependency discovery, reporting, and management tool

Determining problem dependencies in application dependency discovery, reporting, and management tool

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Gaithersburg, MD, United States of America

Muralidharan Balasubramanian