Capital One Services, LLC

Mohammad Shami

Koon Heng Ivan Teo

Fernando San Martin Jorquera

Joshua S Edwards

Yazdan Shirvany

Conor Anstett

Bogdan Nedanov

Yoonseong Kim

Francisco Perez Leon

Volodymyr Orlov

Francisco Perezleon

Joses Nthiga

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

A transaction information aggregation system includes raw transaction and enriched transaction databases. The raw transaction database comprises transaction information for a plurality of transactions, including for each transaction an account holder identifier and at least one transaction parameter. The enriched transaction database comprises an indexed transaction record for each of the plurality of transactions, each record including a plurality of search terms associated with each of the at least one transaction parameter. A transaction information processing server is in data communication with the raw transaction and enriched transaction databases and is configured to receive raw transaction information for a new transaction, parse the raw transaction information to determine, associate search terms with a characteristic for each of the at least one transaction parameter, and establish a new indexed transaction record in the enriched transaction database for the new transaction, the new indexed transaction record including the additional searchable terms.

System and method for interactive transaction information aggregation

Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Neural network systems and methods for generating distributed representations of electronic transaction information

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

Computer-implemented methods and systems are provided for generating a distributed representation of electronic transaction data. Consistent with disclosed embodiments, generation may include receiving electronic transaction data including first and second entity identifiers. Generation may also include generating an output distributed representation by iteratively updating a distributed representation using the electronic transaction data. The distributed representation may include rows corresponding to first entity identifiers and rows corresponding to second entity identifiers. An iterative update may include generating a training sample and an embedding vector using the components and the distributed representation; determining, by a neural network, a predicted category from the embedding vector; and updating the distributed representation using the predicted category and the training sample. The embodiments may also include outputting the output distributed representation to determine authorization of electronic transactions. Disclosed embodiments may also receive an electronic transaction and determine whether to authorize the electronic transaction based on a distributed representation of electronic transaction data.

Computer-implemented methods and systems are provided for generating a distributed representation of electronic transaction data. Generation may include receiving electronic transaction data including first and second entity identifiers. Generation may also include generating an output distributed representation by iteratively updating a distributed representation using the electronic transaction data. The distributed representation may include rows corresponding to first entity identifiers and rows corresponding to second entity identifiers. An iterative update may include generating a training sample and an embedding vector using the components and the distributed representation; determining, by a neural network, a predicted category from the embedding vector; and updating the distributed representation using the predicted category and the training sample. The embodiments may also include outputting the output distributed representation to determine authorization of electronic transactions. Disclosed embodiments may receive an electronic transaction and determine whether to authorize it based on a distributed representation of electronic transaction data.

Growing community of inventors

San Mateo, CA, United States of America

Mohammad Shami