Mike Cook - Escondido CA, US Joseph Blue - San Diego CA, US Christer DiChiara - San Diego CA, US
International Classification:
G06Q 40/00
US Classification:
705044000
Abstract:
A method for identifying a fraudulent account application includes receiving a new account application comprising a plurality of identity-related fields and linking the identity-related fields associated with the new account application with identity-related fields associated with a plurality of historical account applications. The links form a graphical pattern on which statistical analysis can be performed to determine the likelihood that the new account application is fraudulent. The statistical analysis can comprise comparing the graphical pattern to a known, or normal graphical pattern in order to detect differences, or anomalies occurring in the graphical pattern associated with the new account application.
Systems And Methods For Permission Arbitrated Transaction Services
Christer J. DiChiara - San Diego CA, US Kristin M. LeFevre - Orange CA, US Randall P. Mitchum - Tustin CA, US
Assignee:
EXPERIAN INFORMATION SOLUTIONS, INC. - Costa Mesa CA
International Classification:
G06F 21/00
US Classification:
726 4
Abstract:
Systems and methods disclosed allow a permitting party to share personal information with a receiving party. The receiving party may use the information to authenticate the permitting party, assess the permitting party, determine if the permitting party is compatible with one or more other users associated with the receiving party, or validate the permitting party. The permitting party may define how much of the permitting party's personal information is shared, and/or limit the use of the information for one or more specific purposes. A requesting party may also set up criteria for the types of information it wants to review along with the intended use of the information. The systems and methods disclosed also enables permitting parties the ability to grant requesting parties access to requested information.
Systems And Methods For Identifying Synthetic Identities
- Costa Mesa CA, US Mason L. Carpenter - Richmond VA, US Yi He - San Diego CA, US Hua Li - San Diego CA, US Zhixuan Wang - San Diego CA, US Christer DiChiara - Carlsbad CA, US Sophie Liu - San Diego CA, US Eric Haller - Encinitas CA, US Shanji Xiong - San Diego CA, US Honghao Shan - San Diego CA, US Liang Lin - San Diego CA, US Brian Duke - Poway CA, US Chi Zhang - San Diego CA, US Doris Wang - San Diego CA, US Seth Kressin - Carlsbad CA, US
International Classification:
G06N 3/08 G06N 3/04
Abstract:
Systems and methods are provided for implementing machine learning techniques to distinguish a real identity, such as a set of identity information representing a real person, from a synthetic identity that may include portions of real identity information. Attributes regarding a target identity derived from a variety of retrieved data records may be provided as input to multiple machine learning models that generate scores associated with the potential of the target identity being synthetic. The scores may be combined and compared to a threshold to generate a determination of whether the target identity is a synthetic identity.
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