Capital One Services, LLC

David Walter Peters

Matthew Louis Nowak

Keith D Greene

Narender Pashikant

Maqbool A Khatri

Kelvin Wong

James C McGRORY

Rees Draminski

Justin Cheng

Sean O'neill

Alan Christopher Weaver

In some implementations, a data quality system may receive an incoming data set for data quality analysis. The data quality system may obtain data quality validation information from one or more data repositories that form a data quality knowledge base. The data quality system may use one or more machine learning models to evaluate whether the incoming data set has one or more anomalies based on the data quality validation information. The data quality system may generate data quality results for the incoming data set based on an output from the one or more machine learning models. The data quality system may update the data quality knowledge base in accordance with the data quality results for the incoming data set.

Artificial intelligence system for data quality resolution

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate occurrence of an incident based upon previous incidents of an entity. Asset ownership data and development operations tools metric data are compiled and a relationship between the compiled data and an occurrence of previous incidents is determined. A machine learning model predicts relationships between the occurrence of a previous incident and assets data. One or more remediation actions are assigned to an asset and a notification is outputted regarding the same.

Predictive remediation action system

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

Remediation action system

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Richmond, VA, United States of America

David Walter Peters