View source for 18370677. DATA ELEMENT ANALYSIS FOR FRAUD MITIGATION simplified abstract (Wells Fargo Bank, N.A.)

=DATA ELEMENT ANALYSIS FOR FRAUD MITIGATION=


==Organization Name==

[[:Category:Wells Fargo Bank, N.A.|Wells Fargo Bank, N.A.]]


[[Category:Wells Fargo Bank, N.A.]]

==Inventor(s)==

[[:Category:Alan W. Hecht of Chanhassen MN (US)|Alan W. Hecht of Chanhassen MN (US)]][[Category:Alan W. Hecht of Chanhassen MN (US)]]

[[:Category:Andrea Renee Leighton of San Francisco CA (US)|Andrea Renee Leighton of San Francisco CA (US)]][[Category:Andrea Renee Leighton of San Francisco CA (US)]]

==DATA ELEMENT ANALYSIS FOR FRAUD MITIGATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18370677 titled 'DATA ELEMENT ANALYSIS FOR FRAUD MITIGATION

==Simplified Explanation==

The method described in the abstract involves receiving an action notification related to an account, analyzing user action information for fraud detection, determining fraudulent activity based on risk values, and taking fraud mitigation actions.

* The method involves processing circuits within a provider computing system.
* The analysis includes generating risk values for various fraud data risk elements.
* Fraudulent actions are determined based on the risk values calculated.
* Fraud mitigation actions are taken upon confirming fraudulent activity.

== Potential Applications ==
This technology can be applied in various industries such as banking, e-commerce, and financial services to detect and prevent fraudulent activities related to customer accounts.

== Problems Solved ==
This technology helps in identifying and stopping fraudulent actions promptly, thus safeguarding customer accounts and preventing financial losses for both customers and service providers.

== Benefits ==
The benefits of this technology include enhanced security for customer accounts, reduced financial risks for providers, and improved trust and confidence among customers.

== Potential Commercial Applications ==
The potential commercial applications of this technology include fraud detection and prevention services for financial institutions, e-commerce platforms, and other businesses dealing with online transactions.

== Possible Prior Art ==
One possible prior art could be traditional fraud detection methods that rely on manual review processes and rule-based systems to identify suspicious activities in customer accounts.

=== Unanswered Questions ===

=== How does the method handle false positives in fraud detection? ===
The abstract does not mention how the system distinguishes between genuine customer actions and false positives in fraud detection.

=== What measures are in place to protect user privacy during fraud detection analysis? ===
The abstract does not provide details on how user privacy is maintained while analyzing user action information for fraud detection purposes.


==Original Abstract Submitted==

A method includes: receiving, by at least one processing circuit of a provider computing system associated with a provider institution, an action notification regarding an action associated with an account held by a customer at the provider institution; obtaining, by the at least one processing circuit, user action information associated with the action; performing, by the at least one processing circuit, a fraud detection analysis based on the user action information, the fraud detection analysis comprising generating a plurality of individual risk values associated with a plurality of fraud data risk elements based on the user action information; determining, by the at least one processing circuit, that the action is fraudulent based on the plurality of individual risk values associated with the plurality of fraud data risk elements; and performing, by the at least one processing circuit, a fraud mitigation action based on determining that the action is fraudulent.



[[Category:G06Q20/40]]