Credit data analysis

• US 8,930,263 B1
• Filed: 03/14/2013
• Issued: 01/06/2015
• Est. Priority Date: 05/30/2003
• Status: Active Grant

• Current Assignee: Consumerinfo.com Incorporated (Experian plc)
• Original Assignee: Consumerinfo.com Incorporated (Experian plc)
• Inventors: Mahacek, Jeffrey Bryan, Rohn, Janice Anne
• Primary Examiner(s): Tinkler, Muriel
• Application Number:
  US13/826,118
• Time in Patent Office: 663 Days
• Field of Search: 705/4, 705/35, 705/38, 715/854
• US Class Current: 705/38
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

A computer system receives credit information relating to a consumer and a number of high scorers, and determines credit score factors associated with the consumer and the high scorers. The system may construct flippable score factor displays comprising consumer specific information specific to a credit category that may be reversed to display explanatory text regarding how that credit category affects their credit report. The score factor display may include a comparison between the consumer'"'"'s scores and the high scorers'"'"' scores in a number of categories. Scores of high scorers may be periodically refreshed. Additionally, the group of high scorers may be limited according to a particular demographic, such as a geographic location, that may be selectable by a user.

Claims

1. A computer-implemented method of electronic credit data analysis in an electronic environment, the method comprising:

as implemented by one or more computer systems comprising computer hardware and memory, the one or more computer systems configured with specific executable instructions,

receiving a request from a requestor for an electronic consumer credit analysis;

accessing, from an electronic data store over a network,

consumer credit data associated with a plurality of consumers,

wherein the requestor is one of the plurality of consumers;

designating a plurality of high scorers from the plurality of consumers, the plurality of high scorers each having associated credit scores that exceed a predetermined threshold;

calculating, with a processor, for each of one or more credit categories, a high scorers score based at least in part on consumer credit data associated with the plurality of high scorers; and

transmitting, over the network, to the requestor, the electronic consumer credit analysis including the one or more credit categories,

the one or more high scorers scores for the respective credit categories, and consumer credit data associated with the requestor for the respective credit categories.

2. The computer-implemented method of claim 1, wherein calculating a high scorers score for a particular category comprises averaging the consumer credit data of the plurality of high scorers within the particular category.

3. The computer-implemented method of claim 1, wherein the one or more credit categories includes at least one of a number of maxed-out credit cards,

a number of public records, a number of credit inquiries, an average credit card limit, an average age of accounts, a mortgage standing, a number of missed payments, a number of open credit cards, number of installment loans, a credit-to-debt ratio, an oldest account age, and a credit file update time.

4. The computer-implemented method of claim 1, further comprising:

as further implemented by the one or more computer systems,

determining a relevant demographic,

wherein said designating a plurality of high scorers includes designating only high scorers associated with the relevant demographic.

5. The computer-implemented method of claim 4, wherein the relevant demographic includes at least one of an age associated with the requestor, a gender associated with the requestor, an ethnicity associated with the requestor, an employment status associated with the requestor, a geographic location associated with the requestor, a net worth associated with the requestor, and an income level associated with the requestor.

6. The computer-implemented method of claim 4, wherein said relevant demographic comprises a geographic location associated with the requestor, and

wherein the geographic location associated with the requestor includes at least one of a neighborhood in which the requestor lives, a city in which the requestor lives, a county in which the requestor lives, a state in which the requestor lives, and a country in which the requestor lives.

7. The computer-implemented method of claim 1, further comprising:

as further implemented by the one or more computer systems,

causing the electronic consumer credit analysis to be displayed in a user interface on a mobile computing device associated with the requestor.

8. The computer-implemented method of claim 1, further comprising:

determining the one or more credit categories based at least in part on the accessed consumer credit data.

9. The computer-implemented method of claim 1, wherein the high scorers score is periodically recalculated and retransmitted to the requestor.

10. A computer system comprising:

one or more hardware processors in communication with a computer readable medium storing software modules including instructions that are executable by the one or more hardware processors, the software modules including at least;

a user interface module configured to receive, from a consumer,

a request for a credit score analysis;

a data collection module configured to retrieve, from an electronic credit data store, credit information associated with the consumer and a plurality of other consumers; and

an analysis module configured to determine a set of scorers from the plurality of other consumers, and determine, for each of a plurality of score factors, a consumer score and a scorers score,

wherein the user interface module is further configured to provide, to the consumer, an analysis comprising, for each of the plurality of score factors, the consumer score and the scorers score.

11. The computer system of claim 10, wherein the set of scorers comprises at least one of high scorers, low scorers, and average scorers.

12. The computer system of claim 10, wherein the user interface module is further configured to provide, to the consumer, for each of the plurality of score factors, an indication of whether the score factor positively or negatively impacts a credit score of the consumer.

13. The computer system of claim 10, wherein determining a scorers score comprises averaging relevant credit information associated with the set of scorers.

14. The computer system of claim 10, wherein the plurality of score factors includes at least one of a number of maxed-out credit cards, a number of public records, a number of credit inquiries, an average credit card limit, an average age of accounts, a mortgage standing, a number of missed payments, a number of open credit cards, number of installment loans, a credit-to-debt ratio, an oldest account age, and a credit file update time.

15. The computer system of claim 10, wherein the analysis module is further configured to determine a particular demographic, wherein the set of scorers is associated with the particular demographic.

16. The computer system of claim 10, wherein the analysis is displayed in a user interface on a mobile computing device associated with the consumer.

17. The computer system of claim 10, wherein the set of scorers is periodically re-determined.

18. Non-transitory computer storage that comprises executable instructions configured to cause one or more computer processors to perform operations comprising:

receiving first consumer credit information associated with a consumer, wherein the received consumer credit information comprises summary data associated with categories of credit information that impact a credit score of the consumer;

receiving second credit information relating to a plurality of consumers with credit scores above a predetermined threshold, wherein the received second credit information comprises summary data of the plurality of consumers associated with the categories of credit information; and

generating, for display on a touch sensitive computing device, a user interface comprising a plurality of panes associated with respective categories of credit information, wherein each pane is configured to provide a comparison between the first consumer credit information associated with the respective category and the second credit information associated with the respective category.

19. The non-transitory computer storage of claim 18, wherein each pane is further configured to provide an indication of whether credit information of the consumer in a respective category positively or negatively impacts the credit score of the consumer.

20. The non-transitory computer storage of claim 18, wherein each of the plurality of consumers is associated with a particular demographic population.

21. The non-transitory computer storage of claim 20, wherein the particular demographic population includes at least one of an age, a gender, an ethnicity, an employment status, a geographic location, a net worth, and an income level.

22. The non-transitory computer storage of claim 21, wherein the particular demographic population is associated with the consumer.