Method and system for implementing behavior isolating prediction model

• US 9,400,983 B1
• Filed: 05/10/2012
• Issued: 07/26/2016
• Est. Priority Date: 05/10/2012
• Status: Active Grant

• Current Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Original Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Inventors: Andrews, Burton Warren, Gemayel, Nader Michel, Pai, Dee, Sela, Rebecca Jeanette-Paul, Svenson, Joshua D., Weston, Arthur Eladio
• Primary Examiner(s): Kim, Eunhee
• Application Number:
  US13/468,166
• Time in Patent Office: 1,538 Days
• Field of Search: 703/2, 705/7.33, 705/14.4, 705/7.29, 707/603, 707/737, 707/803, 707/805
• US Class Current: 1/1
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

According to an embodiment of the present invention, a computer implemented method and system for isolating variables in a behavior prediction model comprises: identifying a plurality of groups comprising a first group of variables and a second group of variables; building a model, using a computer processor, for capturing an effect of the first group of variables in predicting behavior for customers; building a subsequent stage of the model, using a computer processor, on a second group of variables to neutralize the effect of the first group of variables; displaying results of the model wherein the results minimize the effect of the first group of variables in predicting behavior at a user interface; and identifying a response based on the results for a segment of the customers.

Claims

1. A computer implemented method for isolating variables in a behavior prediction model, the method comprising the steps of:

identifying a plurality of groups comprising a first group of variables and a second group of variables;

building a model, using a computer processor, for capturing an effect of the first group of variables in predicting behavior for customers;

building a subsequent stage of the model, using a computer processor, on a second group of variables to neutralize the effect of the first group of variables by selectively isolating the effect of the first group of variables to remove an influence of the first group of variables on the model;

displaying results of the model wherein the results neutralize the effect of the first group of variables in predicting behavior at a user interface, the results comprising a behavioral prediction that identifies a likelihood of a customer to behave based on the second group of variables relative to customers that match the first group of variables; and

identifying a response based on the results for a segment of the customers.

2. The method of claim 1, wherein the model predicts likelihood of attrition in a banking application.

3. The method of claim 1, wherein the first group of variables comprises demographic variables.

4. The method of claim 3, wherein the second group of variables comprises network strength variables or product relationship variables.

5. The method of claim 3, wherein the second group of variables comprises network strength variables and the plurality of groups comprises a third group of variables comprising product relationship variables.

6. The method of claim 1, further comprising the step of:

combining the results with one or more results of one or more additional iterations of applying the model and viewing the combined results in a matrix format.

7. The method of claim 1, wherein the variables for one or more of the first group and the second group are defined by the user.

8. The method of claim 1, further comprising the step of:

defining an order sequence for applying each group of the plurality of groups to the model.

9. The method of claim 1, wherein the response comprises a tailored marketing offer based on the results.

10. The method of claim 1, wherein a user selects an algorithm for building the model.

11. The method of claim 1, wherein a user interacts with the results of the model by making changes to the model.

12. A computer implemented system for isolating variables in a behavior prediction model, the system comprising:

an input module configured to identify a plurality of groups comprising a first group of variables and a second group of variables;

a modeling module, comprising a computer processor, configured to build a model for capturing an effect of the first group of variables in predicting behavior for customers and further configured to build a subsequent stage of the model on a second group of variables to neutralize the effect of the first group of variables by selectively isolating the effect of the first group of variables to remove an influence of the first group of variables on the model; and

an interface configured to display results of the model wherein the results neutralize the effect of the first group of variables in predicting behavior, the results comprising a behavioral prediction that identifies a likelihood of a customer to behave based on the second group of variables relative to customers that match the first group of variables.

13. The system of claim 12, wherein the model predicts likelihood of attrition in a banking application.

14. The system of claim 12, wherein the first group of variables comprises demographic variables.

15. The system of claim 14, wherein the second group of variables comprises network strength variables or product relationship variables.

16. The system of claim 14, wherein the second group of variables comprises network strength variables and the plurality of groups comprises a third group of variables comprising product relationship variables.

17. The system of claim 12, wherein the results arc combined with one or more results of one or more additional iterations of applying the model and viewing the combined results in a matrix format.

18. The system of claim 12, wherein the variables for one or more of the first group and the second group are defined by the user.

19. The system of claim 12, wherein the input module is further configured to define an order sequence for applying each group of the plurality of groups to the model.

20. The system of claim 12, wherein the response comprises a tailored marketing offer based on the results.

21. The system of claim 12, wherein a user selects an algorithm for building the model.

22. The system of claim 12, wherein a user interacts with the results of the model by making changes to the model.