Credit scoring method and system

• US 20060212386A1
• Filed: 03/15/2006
• Published: 09/21/2006
• Est. Priority Date: 03/15/2005
• Status: Abandoned Application

• Current Assignee: Bridgeforce Incorporated, Global Analytics, Inc. (Ernst & Young LLP)
• Original Assignee: Bridgeforce Incorporated, Global Analytics, Inc. (Ernst & Young LLP)
• Inventors: Gopinathan, Krishna, Willey, Dawn M., Zhang, Ye
• Application Number:
  US11/376,007
• Publication Number:
  US 20060212386A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

A method for calculating probability distributions and probability scores associated with a consumer credit score is disclosed. The probability scores may comprise a bureau volatility score, and bureau error score, and/or a multi-bureau variability score.

Claims

1. A method of assessing a credit risk score comprising:

creating training data from historic credit data, wherein the historic credit data includes a credit risk score of a consumer;

developing a first set of tokens from the training data;

analyzing current credit data for the consumer to develop a second set of tokens, wherein the second set of tokens is a subset of the first set of tokens;

using the second set of tokens to develop a quality score that is indicative of a quality of the consumer'"'"'s credit risk score.

2. The method of claim 1, wherein the historic credit data comprises tradeline information, consumer or business attribute information, public record data credit inquiry data, bureau alert data, or non-tradeline information.

3. The method of claim 1, wherein the quality score comprises a probability distribution or a probability score.

4. The method of claim 1, wherein the quality score comprises a volatility score that predicts variability of the credit risk score over a period of time in the future.

5. The method of claim 4, wherein the volatility score comprises an expected value of the credit risk score'"'"'s statistical variance over at least one future span of time.

6. The method of claim 1, wherein the quality score comprises an error score that determines a likelihood of occurrence of a credit dispute.

7. The method of claim 6, wherein the quality score comprises an error score that determines whether the dispute will result in a change in the consumer'"'"'s credit file.

8. The method of claim 1, wherein the quality score comprises a variability score that determines the variability of the consumer or business credit score among credit reporting agencies.

9. The method of claim 1, further comprising combining the quality score with individual consumer data to determine credit risk score stability and consumer dispute potential.

10. The method of claim 1, further comprising using the quality score to develop underwriting rules for credit approval.

11. The method of claim 1 further comprising generating at plurality of reason codes for the quality score.

12. A method of assessing the volatility of a plurality of a credit risk scores comprising:

creating training data from historic credit data, wherein the historic credit data includes data for a plurality of consumers over a plurality of time periods;

predicting a volatility based on the training data;

using the predicted volatility as a quality score for a credit risk score of one or more of the consumers.

13. A method of assessing a credit risk score comprising:

identifying historic credit data, wherein the historic credit data includes a consumer credit risk score of a consumer; and

developing a quality score based on the historic credit data, wherein the quality score comprises a prediction of a volatility of the consumer credit risk score over a period of time.

14. The method of claim 13, wherein the developing a quality score comprises predicting a distribution of the score over a period of time.

15. A method of assessing a credit risk score comprising:

identifying historic credit data, wherein the historic credit data includes a consumer credit risk score of a consumer; and

developing a quality score based on the historic credit data, wherein the quality score comprises a prediction of a variability of the consumer credit risk score among a plurality of agencies.

16. The method of claim 15, wherein the developing a quality score comprises predicting a probability distribution of the credit risk score over a period of time and combining components of the predicted probability distribution to determine the quality score.