METHOD AND APPARATUS FOR OBTAINING, ORGANIZING, AND ANALYZING MULTI-SOURCE DATA

• US 20060282359A1
• Filed: 06/09/2005
• Published: 12/14/2006
• Est. Priority Date: 06/09/2005
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Nobili, William A., Bonn, Michael S.
• Granted Patent: US 8,271,364 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• 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

Abstract

The tool of the invention is a data gathering and analytical process that uses internal customer data and external CRA data to build a consumer credit market data base and data structure. The internal customer data is classified according to identified Customer Attributes, the CRA data is also classified according to identified Consumer Attributes and the combined data is further classified according to additional Value Added Attributes. The resulting data structure is organized at the individual level such that each individual has associated therewith values for each of the Customer Attributes, Consumer Attributes and Value Added Attributes. The resulting analysis can be used to set institutional strategies and/or to make predictive decisions on individual borrower credit requests.

Claims

1. A list of entries representing individuals, each of said entries having at least one customer attribute, at least one consumer attribute and at least one value added attribute associated therewith, and a value for each of said at least one customer attribute, said at least one consumer attribute, and said at least one value added attribute said value for each attribute being related to each of said entries.

2. The method of claim 1 wherein a first set of entries are customers of an institution.

3. The method of claim 1 wherein the value for the at least one customer attribute is determined from a first source, said first source being maintained by the institution.

4. The method of claim 3 wherein the first source is a data file including information related to customers of the institution.

5. The method of claim 1 wherein a second set of entries are consumers of a product.

6. The method of claim 1 wherein the value for the at least one consumer attribute is determined from a second source, said second source is maintained by an entity separate from the institution.

7. The method of claim 6 wherein the second source is a data file including information related to said consumers of a product.

8. The method of claim 1 wherein the at least one value added attribute includes comparisons between the at least one customer attribute and the at least one consumer attribute.

9. The method of claim 1 wherein the at least one value added attribute includes totals of the values for said at least one customer attribute and said at least one consumer attribute.

10. The method of claim 1 wherein a first set of said entries are customers of an institution and a second set of said entries are consumers of a product offered by the institution.

11. A computer readable medium encoded with a data structure comprising:

a list of entries representing distinct entities, each of said entries having a first set of attributes related to said entity, said first set of attributes being populated with data from a first source;

a second set of attributes related to said entity, said second set of attributes being populated with data from a second source independent from the first source; and

a third set of attributes related to the first and second set of attributes.

12. The method of claim 11 wherein a first set of said entries are customers of an institution.

13. The method of claim 11 wherein said first source is maintained by the institution.

14. The method of claim 11 wherein said first source is a data file including information related to customers of the institution.

15. The method of claim 12 wherein a second set of said entries are consumers of a product, said product being offered by the institution.

16. The method of claim 11 wherein the second source is a data file maintained by an entity separate from the institution.

17. The method of claim 15 wherein the second source is a data file including information related to said consumers of a product.

18. The method of claim 11 wherein the third set of attributes include comparisons between the first set of attributes and the second set of attributes.

19. The method of claim 11 wherein the third set of attributes include totals of the data from the first source and data from the second source.

20. A method for creating a computer readable medium encoded with a data structure comprising:

creating a user file comprising at least two distinct entities;

defining a first set of attributes;

populating said first set of attributes with data from a first source, said data from a first source being related to each of said entities;

defining a second set of attributes;

populating said second set of attributes with data from a second source independent from the first source, said data from a second source being related to each of said entities;

defining a third set of attributes; and

populating said third set of attributes with further data.

21. A method for creating a computer readable medium encoded with a data structure comprising:

defining a first population of entries;

defining a first set of attributes related to said first population of entries;

populating said first set of attributes with data from a first source to create a first data file;

defining a second population of entries;

defining a second set of attributes related to said second population of entries;

populating said second set of attributes with data from a second source to create a second data file;

merging the first data file and the second data file to create a third data file including the first population of entries and the second population of entries and said data from the first source and said data from the second source;

defining a third set of attributes related to said first and second population of entries; and

populating said third set of attributes with data.

22. The method of claim 21 wherein the first population of entries are customers of an institution.

23. The method of claim 21 wherein the first source is a data file maintained by the institution.

24. The method of claim 21 wherein the first source is a data file including information related to customer'"'"'s of the institution.

25. The method of claim 22 wherein the second population of entries are consumers of a product.

26. The method of claim 22 wherein the second source is a data file maintained by an entity separate from the institution.

27. The method of claim 22 wherein the second source is a data file including information related to said consumers of a product.

28. The method of claim 22 wherein the third set of attributes include comparisons between the first set of attributes and the second set of attributes.

29. The method of claim 22 wherein the third set of attributes include totals of the data from the first source and data from the second source.

30. A method for analyzing data comprising:

for a first time period;

defining a first population of entries;

defining a first set of attributes related to said first population of entries;

populating said first set of attributes with data from a first source to create a first data file;

defining a second population of entries;

defining a second set of attributes related to said second population of entries;

populating said second set of attributes with data from a second source to create a second data file;

merging the first data file and the second data file to create a third data file including the first population of entries and the second population of entries and said data from the first source and said data from the second source;

defining a third set of attributes related to said first and second population of entries in said third data file; and

populating said third set of attributes with data;

for a second time period;

defining a third population of entries;

defining a fourth set of attributes related to said third population of entries;

populating said fourth set of attributes with data from the first source to create a fourth data file;

defining a fourth population of entries;

defining a fifth set of attributes related to said fourth population of entries;

populating said fifth set of attributes with data from a second source to create a fifth data file;

merging the fourth data file and the fifth data file to create a sixth data file including the third population of entries and the fourth population of entries and said data from the first source and said data from the second source;

defining a sixth set of attributes related to said third and fourth population of entries in said sixth data file; and

populating said third set of attributes with data; and

comparing the attributes in the third data file with the attributes in the sixth data file.

31. A method for analyzing data comprising:

defining a first population of entries;

defining a first set of attributes related to said first population of entries;

populating said first set of attributes with data from a first source to create a first data file;

defining a second population of entries;

defining a second set of attributes related to said second population of entries;

populating said second set of attributes with data from a second source to create a second data file;

merging the first data file and the second data file to create a third data file including the first population of entries and the second population of entries and said data from the first source and said data from the second source;

defining a third set of attributes related to said first and second population of entries; and

populating said third set of attributes with data; and

scaling the data to account for duplicative data.

32. The method of claim 31 wherein scaling the data includes scaling the data to account for duplicative data.

33. The method of claim 31 wherein scaling the data includes scaling the data from a sample size to a total market size.