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

Abstract

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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 60 / 931,902, titled METHODS AND SYSTEMS FOR MODELING NEVER-PAY POPULATION and filed on May 25, 2007, which is hereby incorporated by reference in its entirety, specifically the systems and methods for modeling the never-pay population as disclosed therein.BACKGROUND[0002]1. Field of the Invention[0003]This disclosure generally relates to data filters for modeling and processing credit report data and other data, and more particularly to improved systems and methods for generating and using data filters configured to conduct customer profiling and customer analysis relating to modeling, identifying, and / or predicting the never-pay population.[0004]2. Description of the Related Art[0005]Various financial service entities provide credit accounts, such as, for example, mortgages, automobile loans, credit card accounts, and the like, to consumers and or businesses. Prior to...

AI Technical Summary

Benefits of technology

[0014]For purposes of this summary, certain aspects, advantages, and novel features of the invention are described herein. It is to be understood that not necessarily all such aspects, advantages, and features may be employed and/or achieved in accordance with any particular embodiment of

Problems solved by technology

Although the never-pay population is not always large (however, it can be a large population for certain financial firms, for example, those firms serving the sub-prime market or the like), it is a costly population to financial service providers and other entities.

Traditional scoring models do not provide the necessary insight to identify the never-pay population.

Additionally, the attributes and / or reasons that contribute to the never-pay population are difficult to identify for some financial service providers because of their limited resources and the complexity of analyzing the never-pay population.

Accordingly, these never-pay accounts are not identified early in the process, and are treated as typical credit loss and are often written off as bad debt.

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