Experian Information Solutions, Inc.

Christopher J Celka

Cristian Robinson Rojas

Data filters, models, and/or profiles for identifying and/or predicting the never-pay population (for example, those customers that make a request for credit and obtain the credit instrument but over the life of the account, never make a payment) can be useful to various commercial entities, such as those issuing mortgages, home equity lines of credit, consumer or business lines of credit, automobile loans, credit card accounts, or those entities providing services, such as utility services, phone services, and the like.

System and method for automated detection of never-pay data sets

The embodiments illustrated herein provide systems and methods for predicting or identifying early life account fraud at a point-in-time within existing account portfolios. The identity-level linking of inquiry data described herein allow for detecting inconsistent and/or suspicious use of identity data elements across multiple applications that traditional fraud detection systems may consider to be different consumers. The system can be configured to calculate or generate a fraud score after a credit account is booked and/or opened by a financial institution or other third party. Even though the likelihood of fraud is assessed at the time when an applicant applies for credit, there may still be some applicants that successfully pass through authentication and/or fraud tools.

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Suwanee, GA, United States of America

Christopher J Celka