DETERMINING A PERSONALIZED FUSION SCORE

• US 20130173452A1
• Filed: 12/28/2012
• Published: 07/04/2013
• Est. Priority Date: 12/29/2011
• Status: Abandoned Application

• Current Assignee: Equifax, Inc.
• Original Assignee: Equifax, Inc.
• Inventors: O'Connor, Martin, Zhu, Qianqiu, Richard, Daniel
• Application Number:
  US13/729,858
• Publication Number:
  US 20130173452A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

Various embodiments of the present invention provide systems and methods for determining a personalized fusion score. In certain embodiments, the systems and methods are configured for calculating preliminary fused scores for consumers at least in part by applying a first score fusion technique across the sample of consumer data. Segmentation scores are then calculated based at least in part upon the preliminary fused scores. In those and other embodiments, the segmentation scores enable creation of a plurality of cluster subsets within the sample of consumer data. In certain embodiments cluster subsets are defined at least in part by a particular score mix, while in other embodiments subsets are defined at least in part by respective score fusion techniques that prove optimal for each subset. Further, in various embodiments, application of multiple score fusion techniques across respective cluster subsets provides personalized fusion scores for the consumers in each respective cluster subset.

Claims

1. A computer-implemented method for determining a personalized fusion score, said method comprising the steps of:

(a) receiving a sample of consumer data stored in a memory, said sample of consumer data comprising a plurality of consumers;

(b) calculating, via at least one computer processor, preliminary fused scores for at least two consumers in said sample of consumer data, said sample of consumer data comprising at least two predictive scores for said at least two consumers in said sample of consumer data, and said preliminary fused scores being calculated at least in part by applying a first score fusion technique to said at least two predictive scores for said at least two consumers in said sample of consumer data;

(c) calculating, via the at least one computer processor, segmentation scores for said at least two consumers in said sample of consumer data, said segmentation scores being calculated based at least in part upon said preliminary fused scores;

(d) creating, via the at least one computer processor, a plurality of cluster subsets within said sample of consumer data based on said segmentation scores, each of the plurality of cluster subsets comprising at least one of said at least two consumers in said sample of consumer data;

(e) determining, via the at least one computer processor, an optimal score fusion technique for at least one of said plurality of cluster subsets, said optimal score fusion technique being determined independently from said first score fusion technique applied to said at least two predictive scores for said at least two consumers in said sample of consumer data; and

(f) calculating, via the at least one computer processor, a personalized fusion score for at least one consumer in at least one of said plurality of cluster subsets, said personalized fusion score being calculated by applying said optimal fusion score technique to said at least two predictive scores for said at least one consumer in said at least one of said plurality of cluster subsets.

2. The computer-implemented method of claim 1, wherein the at least two predictive scores comprise at least one of a credit score, a bankruptcy score, and an affordability score.

3. The computer-implemented method of claim 1, wherein said first score fusion technique is selected from the group consisting of:

a gravitational fusion model, a displaced force fusion model, a regression model, a decision tree model, and a neural network model.

4. The computer-implemented method of claim 1, wherein the segmentation scores are further calculated based at least in part upon a plurality of additional attributes associated with said at least two consumers of said sample of consumer data.

5. The computer-implemented method of claim 1, wherein the step of creating the plurality of cluster subsets within said sample of consumer data further comprises determining, via the at least one computer processor, whether sufficiently distinct score mixes exists between at least two of the plurality of cluster subsets.

6. The computer-implemented method of claim 5, further comprising, when said sufficiently distinct score mixes do not exist between two or more of the plurality of cluster subsets, the step of redistributing, via the at least one computer processor, one or more of the plurality of cluster subsets within said sample of consumer data.

7. The computer-implemented method of claim 1, wherein the step of creating the plurality of cluster subsets within said sample of consumer data further comprises determining, via the at least one computer processor, whether sufficiently distinct optimal score fusion techniques exist between at least two of the plurality of cluster subsets.

8. The computer-implemented method of claim 7, further comprising, when said sufficiently distinct optimal statistical techniques do not exist between two or more of the plurality of cluster subsets, the step of redistributing, via the at least one computer processor, one or more of the plurality of cluster subsets within said sample of consumer data.

9. The computer-implemented method of claim 1, further comprising the step of, via the at least one computer processor, assessing a performance rating of the personalized fusion score at least by comparing the personalized fusion score to an incumbent benchmark solution.

10. The computer-implemented method of claim 1, wherein the step of calculating said segmentation scores for the at least two consumers further comprises the sub-steps of:

retrieving additional attributes for said at least two consumers in said sample of consumer data; and

applying the first score fusion technique to the preliminary fused scores and the additional attributes for said at least two consumers in said sample of consumer data to calculate said segmentation scores for said at least two consumers in said sample of consumer data.

11. The computer-implemented method of claim 10, wherein the additional attributes for said at least two consumers of said sample of consumer data comprise at least one of the following:

one or more geographic attributes, one or more demographic attributes, one or more personal attributes, and one or more financial attributes.

12. A system for determining a personalized fusion score, said system comprising:

one or more memory storage areas; and

one or more computer processors that are configured to receive data stored in the one or more memory storage areas, wherein the one or more computer processors are configured for;

calculating preliminary fused scores for at least two consumers in a sample of consumer data, said sample of consumer data comprising at least two predictive scores for said at least two consumers in said sample of consumer data, and said preliminary fused scores being calculated at least in part by applying a first score fusion technique to said at least two predictive scores for said at least two consumers in said sample of consumer data;

calculating segmentation scores for said at least two consumers in said sample of consumer data, said segmentation scores being calculated based at least in part upon said preliminary fused scores;

creating a plurality of cluster subsets within said sample of consumer data based on said segmentation scores, each of the plurality of cluster subsets comprising at least one of said at least two consumers in said sample of consumer data;

determining an optimal score fusion technique for at least one of said plurality of cluster subsets, said optimal score fusion technique being determined independently from said first score fusion technique applied to said at least two predictive scores for said at least two consumers in said sample of consumer data; and

calculating a personalized fusion score for at least one consumer in at least one of said plurality of cluster subsets, said personalized fusion score being calculated by applying said optimal fusion score technique to said at least two predictive scores for said at least one consumer in said at least one of said plurality of cluster subsets.

13. The system for determining a personalized fusion score of claim 12, wherein the at least two predictive scores comprise at least one of a credit score, a bankruptcy score, and an affordability score.

14. The system for determining a personalized fusion score of claim 12, wherein said first score fusion technique is selected from the group consisting of:

a gravitational fusion model, a displaced force fusion model, a regression model, a decision tree model, and a neural network model.

15. The system for determining a personalized fusion score of claim 12, wherein the segmentation scores are further calculated based at least in part upon a plurality of additional attributes associated with said at least two consumers of said sample of consumer data.

16. The system for determining a personalized fusion score of claim 12, wherein the processor is further configured, when creating the plurality of cluster subsets, to determine whether sufficiently distinct score mixes exists between at least two of the plurality of cluster subsets.

17. The system for determining a personalized fusion score of claim 16, wherein the processor is further configured, when said sufficiently distinct score mixes do not exist, to redistribute one or more of the plurality of cluster subsets across said sample of consumer data.

18. The system for determining a personalized fusion score of claim 12, wherein the processor is further configured, when creating the plurality of cluster subsets, to determine whether sufficiently distinct optimal score fusion techniques exist between at least two of the plurality of cluster subsets.

19. The system for determining a personalized fusion score of claim 18, wherein the processor is further configured, when sufficiently distinct optimal score fusion techniques do not exist, to redistribute one or more of the plurality of cluster subsets across said sample of consumer data.

20. The system for determining a personalized fusion score of claim 12, wherein the at least one computer processor is further configured to assess a performance rating of the personalized fusion score at least by comparing the personalized fusion score to an incumbent benchmark solution.

21. The system for determining a personalized fusion score of claim 12, wherein the at least one computer processor is further configured, in calculating said segmentation scores for at least two consumers in a sample of consumer data, to:

retrieve additional attributes for said at least two consumers in said sample of consumer data; and

apply the first score fusion technique to the preliminary fused scores and the additional attributes for said at least two consumers in said sample of consumer data to calculate said segmentation scores for said at least two consumers in said sample of consumer data.

22. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:

an executable portion configured for calculating preliminary fused scores for at least two consumers in a sample of consumer data, said sample of consumer data comprising at least two predictive scores for said at least two consumers in said sample of consumer data, and said preliminary fused scores being calculated at least in part by applying a first score fusion technique to said at least two predictive scores for said at least two consumers in said sample of consumer data;

an executable portion configured for calculating segmentation scores for said at least two consumers in said sample of consumer data, said segmentation scores being calculated based at least in part upon said preliminary fused scores;

an executable portion configured for creating a plurality of cluster subsets within said sample of consumer data based on said segmentation scores, each of the plurality of cluster subsets comprising at least one of said at least two consumers in said sample of consumer data;

an executable portion configured for determining an optimal score fusion technique for at least one of said plurality of cluster subsets, said optimal score fusion technique being determined independently from said first score fusion technique applied to said at least two predictive scores for said at least two consumers in said sample of consumer data; and

an executable portion configured for calculating a personalized fusion score for at least one consumer in at least one of said plurality of cluster subsets, said personalized fusion score being calculated by applying said optimal fusion score technique to said at least two predictive scores for said at least one consumer in said at least one of said plurality of cluster subsets.

23. The computer program product of claim 22, wherein the executable portion configured for calculating score values for at least two consumers in a sample of consumer data is further configured for:

retrieving additional attributes for said at least two consumers in said sample of consumer data; and

applying the first score fusion technique to the preliminary fused scores and the additional attributes for said at least two consumers in said sample of consumer data to calculate said segmentation scores for said at least two consumers in said sample of consumer data.

24. The computer program product of claim 22, wherein said first score fusion technique is selected from the group consisting of:

a gravitational fusion model, a displaced force fusion model, a regression model, a decision tree model, and a neural network model.

25. The computer program product of claim 22, wherein the segmentation scores are further calculated based at least in part upon a plurality of additional attributes associated with said at least two consumers of said sample of consumer data.

26. The computer program product of claim 22, wherein, when creating the plurality of cluster subsets, the executable portion is further configured to:

determine whether sufficiently distinct score mixes exists between at least two of the plurality of cluster subsets; and

when said sufficiently distinct score mixes do not exist, to redistribute one or more of the plurality of cluster subsets across said sample of consumer data.

27. The computer program product of claim 22, wherein, when creating the plurality of cluster subsets, the executable portion is further configured to:

determine whether sufficiently distinct optimal score fusion techniques exist between at least two of the plurality of cluster subsets; and

when sufficiently distinct optimal score fusion techniques do not exist, to redistribute one or more of the plurality of cluster subsets across said sample of consumer data.