Capital One Services, LLC

Capital One Financial Corporation

John Fields

Lawrence H Douglas

Lin Ni Lisa Cheng

Thomas S Poole

James M Ashfield

Bharath Bhushan Sreeravindra

Justin Anthony Bowers

Alexis Howard

Wade Rance

Matthew Michael Burke

Drew Cogswell

Justin Bowers

In some aspects, a computing system may authenticate a user before blockchain functions may be performed and provide obstacles to prevent brute force attacks in decentralized computing systems or other distributed computing system environments. The computing system may use a smart contract to assist with authenticating a user. As authentication attempts fail, the computing system may increase a network usage cost associated with authentication. By doing so, the security of a computing system may be improved by making it cost-prohibitive for malicious actors to perform brute force attacks. Further, a computing system may use a variety of biometric information that may provide additional verification and security when authentication is performed.

User authentication in decentralized computing system environment

Disclosed embodiments may include a system for generating aggregate records. The system may retrieve data corresponding to a first user. The system may receive, from the first user, first sharing preference(s) associated with a second user. The system may extract first data subset(s) from the data based on the first sharing preference(s). The system may generate block(s) of a blockchain ledger. The system may append the first data subset(s) to at least a first block of the block(s) of the blockchain ledger. The system may provide the second user with access to the first data subset(s) on the at least first block.

Systems and methods for generating aggregate records

Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Leveraging blockchain based machine learning modeling for expense categorization

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Henrico, VA, United States of America

John Fields