Id Analytics, Inc.

Fair Isaac Corporation

Symantec Corporation

Xuhui Shao

Tao Hong

Allen P Jost

Jianjun Xie

Joseph P Milana

Larry Peranich

Paul Charles Dulany

Russell Anderson

Ricardo M Dungca

Khosrow M Hassibi

James C Baker

Ted E Dunning

Stephen V Coggeshall

Alan Tsang

Christer J DiChiara

Mike Cook

Joe Blue

A method for determining a likelihood of fraud associated with an input identity record is disclosed herein. The disclosed method contemplates determining characteristics of the input identity record by examining content of one or more fields of the input identity record. Historical identity records related to the input identity record may then be retrieved so as to define a set of linked identity records. The method further includes computing one or more network-based features of the set of linked identity records. A fraud score may then be generated based upon the characteristics of the input identity record and the one or more network-based features.

System and method for identity-based fraud detection

Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Detecting and measuring risk with predictive models using content mining

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.

System and method for identity-based fraud detection using a plurality of historical identity records

System and method for identity-based fraud detection through graph anomaly detection

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 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.

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San Diego, CA, United States of America

Xuhui Shao