SYSTEMS AND METHODS FOR DETERMINING THIN-FILE RECORDS AND DETERMINING THIN-FILE RISK LEVELS

• US 20080255975A1
• Filed: 10/12/2007
• Published: 10/16/2008
• Est. Priority Date: 04/12/2007
• Status: Active Grant

• Current Assignee: Experian Marketing Solutions, LLC (Experian plc)
• Original Assignee: Experian Marketing Solutions Incorporated (Experian plc)
• Inventors: Hjermstad, Erik, Hadenfeldt, Nancy A., Chaudhuri, Anamitra
• Granted Patent: US 7,742,982 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• 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
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

In some embodiments, systems and methods are disclosed for generating filters to determine whether a consumer is likely to have a scoreable credit record based on non-credit data, and to determine a potential risk level associated with an unscoreable credit record based on non-credit data. Existing scoreable and unscoreable records are compared to determine factors correlated with having an unscoreable record, and a multi-level filter is developed. Unscoreable records having at least one entry are compared to determine whether they are “good” or “bad” risks, factors correlated with either condition are determined, and a filter is developed. The filters can be applied to records comprising demographic data to determine consumers that are likely to have unscoreable records but represent good risks.

Claims

1. A computing system, comprising:

a computer-readable storage comprising a database and storing a plurality of records, the plurality of records comprising demographic data and credit data;

a processor configured to access the plurality of records in the computer-readable storage and configured to filter the records according to whether the records include a predetermined amount of credit data;

a thin-file module configured to access the filtered records and determine demographic data that is correlated to the records that include the predetermined amount of credit data; and

a filter configured to predict the propensity of records including the predetermined amount of credit data based on the correlated demographic data.

2. The system of claim 1, further comprising a communications link configured to receive a set of customer demographic data.

3. The system of claim 2, wherein the filter is further configured to generate a score corresponding to the propensity of the customer having the predetermined amount of credit data by filtering the set of customer demographic data according to the correlated demographic data.

4. The system of claim 1, wherein the predetermined amount of credit data is one entry.

5. The system of claim 1, wherein the records that include less than the predetermined amount of credit data are thin-file records.

6. A computer implemented method of developing a filter for thin-file records comprising:

accessing a database storing a plurality of records, each of the records comprising demographic data fields and credit data fields;

identifying a first subset of the plurality of records stored in the database, the first subset corresponding a first type comprising essentially those records that include a predetermined amount of credit data;

identifying a second subset of the plurality of records stored in the database, the second subset of records corresponding to a second type comprising essentially those records that do not include the predetermined amount of credit data;

determining demographic data that is correlated to the first type of records; and

developing a filter that predicts the propensity of a record being of the first type based on the correlated demographic data.

7. The computer implemented method of claim 6, further comprising:

providing a thin-file filter, the thin-file filter configured to associate selected demographic data with a value;

providing a set of customer data, the customer data comprising demographic data including demographic data corresponding to at least a portion of the selected demographic data;

generating a score by applying the thin-file filter to the customer data.

8. The computer implemented method of claim 6, wherein the predetermined amount of credit data is one transaction entry.

9. The computer implemented method of claim 6, wherein the second subset is a thin-file collection.

10. A storage medium having a computer program stored thereon for causing a suitably programmed system to process computer-program code by performing the computer implemented method of claim 6 when such program is executed on the system.

11. A computing system, comprising:

a computer-readable storage comprising a database and storing a plurality of records, the plurality of records comprising demographic data and credit data, and wherein the records comprise thin-file records;

a processor configured to access the plurality of thin-file records in the computer-readable storage and configured to filter the thin-file records according to whether the credit data corresponds to a positive credit activity

a thin-file module configured to access the filtered thin-file records and determine demographic data that is correlated to the records having credit data corresponding to the positive credit activity; and

a filter configured to predict the propensity of thin-file records having credit data corresponding to the positive credit activity after a predetermined period of time based at least in part on the correlated demographic data.

12. The computing system of claim 11, further comprising a communications link configured to receive a set of customer demographic data associated with a thin-file customer.

13. The computing system of claim 12, wherein the filter is further configured to generate a score corresponding to the propensity of the thin-file customer'"'"'s credit record corresponding to the positive credit activity after the predetermined period of time by filtering the set of customer demographic data according to the correlated demographic data.

14. The computing system of claim 13, wherein the score indicates a credit risk.

15. The computing system of claim 11, wherein the thin-file records having credit data that does not correspond to the positive credit activity are determined to have a high credit risk.

16. A computer implemented method of generating a filter for determining a risk level associated with a thin-file record, comprising:

accessing a database storing a plurality of thin-file records, the thin-file records comprising demographic data and credit data;

monitoring the plurality of thin-file records in order to determine a status of each of the records;

determining demographic data that is correlated to the status of each of the records; and

developing a filter that predicts the probable status of a record based on the correlated demographic data.

17. The computer implemented method of claim 16, further comprising:

providing a set of customer data associated with a thin-file customer, the customer data comprising demographic data including demographic data corresponding to at least a portion of the correlated demographic data.

18. The computer implemented method of claim 17, further comprising:

generating a score by applying the filter to the customer data.

19. The computer implemented method of claim 16, wherein the probable status indicates a credit risk.

20. A storage medium having a computer program stored thereon for causing a suitably programmed system to process computer-program code by performing the computer implemented method of claim 16 when such program is executed on the system.