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

• US 20120158575A1
• Filed: 09/19/2011
• Published: 06/21/2012
• 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: Chaudhuri, Anamitra, Hadenfeldt, Nancy A., Hjermstad, Erik
• Granted Patent: US 8,271,378 B2
• Time in Patent Office: Days
• US Class Current: 705/38
• 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. (canceled)

2. A computing system, comprising:

a processor configured to access a plurality of records and configured to filter the records according to whether the records include less than a predetermined amount of credit data;

a thin-file propensity module executing in the processor and configured to;

access the filtered records and determine demographic characteristics that are correlated to the records that include less than the predetermined amount of credit data; and

based on the correlated demographic characteristics, predict a likelihood that a first record includes less than the predetermined amount of credit data; and

a risk assessment module executing in the processor and configured to determine a risk associated with the first record by at least;

identifying a level of risk associated with one or more characteristics associated with the first record, wherein the one or more characteristics include a demographic characteristic associated with the first record; and

based on the identified level of risk associated with the one or more characteristics associated with the first record, determining a risk score associated with the first record.

3. The computing system of claim 2, wherein the demographic characteristic comprises one or more of residence address data, age data, household data, marital status data, delinquent data for consumers in a geographic area, and data related to length of residency.

4. A method of identifying a level of risk associated with a thin-file credit record, comprising:

filtering a first set of records to obtain a set of filtered records comprising a subset of the first set of records which include less than a predetermined amount of credit data;

reviewing one or more demographic characteristics associated with the set of filtered records to identify at least one thin-file demographic characteristic which, if associated with a particular file, is indicative of that particular file being likely to include less than the predetermined amount of credit data;

predicting, by at least one computer processor, that a first record in a second set of one or more records is likely to include less than the predetermined amount of credit data based at least in part on whether the first record is associated with the at least one thin-file demographic characteristic;

identifying a level of risk associated with at least one demographic characteristic associated with the first record; and

based on the identified level of risk associated with the at least one demographic characteristic, determining a risk score associated with the first record.

5. The method of claim 4, wherein the at least one thin-file demographic characteristic comprises one or more of residence address data, age data, household data, marital status data, delinquent data for consumers in a geographic area, and data related to length of residency.

6. A computing system, comprising:

a processor configured to access a first set of records and to filter the first set of records to obtain a set of filtered records comprising a subset of the first set of records which include less than a predetermined amount of credit data;

a thin-file propensity module executing in the processor and configured to;

access the set of filtered records;

review one or more demographic characteristics associated with the set of filtered records to identify at least one thin-file demographic characteristic which, if associated with a particular file, is indicative of that particular file being likely to include less than the predetermined amount of credit data;

predict that a first record in a second set of one or more records is likely to include less than the predetermined amount of credit data based at least in part on whether the first record is associated with the at least one thin-file demographic characteristic; and

a risk assessment module executing in the processor and configured to determine a level of risk associated with the first record that includes less than the predetermined amount of credit data by at least;

identifying a level of risk associated with at least one demographic characteristic associated with the first record; and

based on the identified level of risk associated with the demographic characteristic, determining a risk score associated with the first record.

7. The computing system of claim 6, wherein the at least one thin-file demographic characteristic comprises one or more of residence address data, age data, household data, marital status data, delinquent data for consumers in a geographic area, and data related to length of residency.