SYSTEMS AND METHODS FOR ANALYZING DATA

• US 20090099960A1
• Filed: 12/18/2008
• Published: 04/16/2009
• Est. Priority Date: 03/10/2006
• Status: Abandoned Application

• Current Assignee: Experian Information Solutions Incorporated (Experian plc)
• Original Assignee: Experian-Scorex, LLC (Experian plc)
• Inventors: Wang, Chien-Wei, Robida, Chuck
• Application Number:
  US12/338,871
• Publication Number:
  US 20090099960A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Information regarding individuals that fit a bad performance definition, such as individuals that have previously defaulted on a financial instrument or have declared bankruptcy, is used to develop a model that is usable to determine whether an individual that does not fit the bad performance definition is more likely to subsequently default on a financial instrument or to declare bankruptcy. The model may be used to generate a score for each individual, and the score may be used to segment the individual into a segment of a segmentation structure that includes individuals with related scores, where segments may include different models for generating a final risk score for the individuals assigned to the particular segments. Thus, the segment to which an individual is assigned, which may be determined based at least partly on the score assigned to the individual, may affect the final risk score that is assigned to the individual.

Claims

1. A computer readable medium having stored thereon a computer program that embodies a method of generating a model for determining an individual'"'"'s propensity to enter either a first failure mode or a second failure mode, wherein the computer program is configured for storage on a computing system in order to transform the computing system into a special purpose computing system configured to perform the method comprising:

receiving information defining a bad performance definition, wherein the bad performance definition is defined to include individuals that have characteristics of one or more of the first failure mode and the second failure mode;

receiving observation data regarding a plurality of individuals fitting the bad performance definition, the observation data indicating characteristics of the individuals at an observation time;

receiving outcome data regarding the plurality of individuals fitting the bad performance definition, the outcome data indicating characteristics of the individuals fitting the bad performance definition during an outcome period, the outcome period beginning after the observation time; and

storing at least some of the observation data and the outcome data in a storage device;

transforming the observation data and the outcome data into a model configured to determine a likelihood that an individual not fitting the bad performance definition will enter the first failure mode or if the individual will enter the second failure mode.

2. The computer readable medium of claim 1, wherein the observation time is about 24 months prior to generation of the model.

3. The computer readable medium of claim 1, wherein the outcome period is a period of about 24 months prior to generation of the model, but exclusive of the observation time.

4. A computerized method of generating a model for determining an individual'"'"'s propensity to enter either a first failure mode or a second failure mode, the method comprising:

receiving information that defines characteristics of a bad performance definition so that the bad performance definition includes individuals that have characteristics of one or more of a first failure mode and a second failure mode;

receiving observation data regarding a plurality of individuals fitting the bad performance definition, the observation data including a snapshot of financial and demographic information associated with respective individuals at a previous point in time T−

X, where T is the current month and X is a number of months;

recording outcome data regarding the financial behavior of the individuals during the time from T−

X+1 to T; and

comparing the observation data associated with the individuals at time T−

X and the outcome data recorded during the period T−

X to T in order to generate a model usable to determine a likelihood that an individual not fitting the bad performance definition will enter a first failure mode or if the individual will enter the second failure mode,

wherein the computerized method is embodied in computer source code that is loaded into one or more memories of a computing systems in order to modify the content of the one or more memories to cause the computing system to perform the computerized method.

5. The method of claim 4, wherein the first failure mode comprises filing for bankruptcy and the second failure mode comprises defaulting on a financial instrument.

6. The method of claim 4, wherein the first failure mode comprises defaulting on an installment loan and the second failure mode comprises defaulting on a revolving loan.

7. The method of claim 4, wherein the first failure mode comprises defaulting on a bank loan and the second failure mode comprises defaulting on an automobile loan.

8. The method of claim 4, wherein the observation time is about 24 months prior to generation of the model.

9. The method of claim 8, wherein the outcome period is a period of about 24 months prior to generation of the model, but exclusive of the observation time.

10. A computer system configured to generate a behavioral model that is indicative of an individual'"'"'s propensity to enter either a first failure mode or a second failure mode, the computer system comprising:

one or more input devices for receiving information defining a bad performance definition so that the bad performance definition includes individuals that have characteristics of one or more of the first and second failure modes;

one or more interfaces for receiving observation data regarding a plurality of individuals fitting the bad performance definition, the observation data indicating characteristics of the individuals at an observation time, and for receiving outcome data regarding the plurality of individuals fitting the bad performance definition, the outcome data indicating characteristics of the individuals fitting the bad performance definition during an outcome period, the outcome period beginning after the observation time; and

a profile module configured to compare the observation data and the outcome data in order to generate a model usable to determine a likelihood that an individual not fitting the bad performance definition will enter a first failure mode or if the individual will enter the second failure mode,

11. The computer system of claim 10, wherein the observation data comprises one or more of demographic data and financial data regarding individuals

12. The computer system of claim 10, wherein the profile module is further configured to apply the generated model to information regarding a modeled individual that does not fit the bad performance definition and to determine whether the modeled individual is more likely to later enter the first failure mode or the second failure mode.

13. A computerized method of generating a model for determining an individual'"'"'s propensity to enter either a first failure mode or a second failure mode, the method comprising:

defining a bad performance definition to include individuals that have characteristics of one or more of the first and second failure modes;

receiving observation data regarding a plurality of individuals fitting the bad performance definition, the observation data indicating characteristics of the individuals at an observation time;

receiving outcome data regarding the plurality of individuals fitting the bad performance definition, the outcome data indicating characteristics of the individuals fitting the bad performance definition during an outcome period, the outcome period beginning after the observation time; and

comparing the observation data and the outcome data in order to generate a model usable to determine a likelihood that an individual not fitting the bad performance definition will enter a first failure mode or if the individual will enter the second failure mode,

wherein the method is performed by one or more computing systems.