17344653. CROSS-ENTITY TRANSACTION ANALYSIS simplified abstract (Wells Fargo Bank, N.A.)
Contents
CROSS-ENTITY TRANSACTION ANALYSIS
Organization Name
Inventor(s)
Kristine Ing Kushner of Orinda CA (US)
John T. Wright of Benicia CA (US)
CROSS-ENTITY TRANSACTION ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17344653 titled 'CROSS-ENTITY TRANSACTION ANALYSIS
This disclosure outlines techniques for analyzing data across multiple financial institutions, specifically transaction data.
- Receiving transaction data from different entities.
- Identifying transaction data associated with an account holder across multiple accounts.
- Assessing the likelihood of fraud on any of the accounts.
- Taking action based on the analysis.
Potential Applications: - Fraud detection in financial transactions. - Cross-institutional data analysis for regulatory compliance.
Problems Solved: - Streamlining analysis of transaction data across multiple institutions. - Enhancing fraud detection capabilities.
Benefits: - Improved fraud prevention. - Enhanced regulatory compliance. - Efficient data analysis processes.
Commercial Applications: Title: Cross-Institutional Data Analysis for Fraud Detection This technology can be utilized by financial institutions, regulatory bodies, and compliance departments to enhance fraud detection and ensure regulatory compliance in financial transactions.
Questions about Cross-Institutional Data Analysis for Fraud Detection: 1. How does this technology improve fraud detection in financial transactions? 2. What are the potential applications of cross-institutional data analysis in the financial sector?
Frequently Updated Research: Stay updated on advancements in fraud detection technologies and regulatory compliance requirements to ensure the effectiveness of this cross-institutional data analysis technique.
Original Abstract Submitted
This disclosure describes techniques for performing cross-institution analysis of data, including analysis of transaction data occurring across multiple financial institutions. In one example, this disclosure describes a method that includes receiving a first set of transaction data associated with accounts at the first entity; receiving a second set of transaction data associated with accounts at the second entity; identifying transaction data associated with an account holder having a first account at the first entity and a second account at the second entity, wherein the transaction data associated with the account holder includes information about transactions occurring on the first account and information about transactions occurring on the second account; assessing a likelihood of fraud having occurred on at least one of the first account or the second account; and performing an action.
