Credit line optimization

• US 20090313163A1
• Filed: 12/14/2004
• Published: 12/17/2009
• Est. Priority Date: 02/13/2004
• Status: Abandoned Application

• Current Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Original Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Inventors: Zhao, Feng, Wang, Ming-huan
• Application Number:
  US11/011,651
• Publication Number:
  US 20090313163A1
• Time in Patent Office: Days
• US Class Current: 705/38
• 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

In a system for assigning a credit line to a credit card application, the system receives a plurality of credit card applications each having applicant information. For each application, the system retrieves credit bureau information. The applicant information and the credit bureau information are used to model the likely behavior of the corresponding applicant. The applications are clustered into one or more clusters according to the modeled behavior. For each cluster of applications, financial measures are forecasted and analyzed to determine the optimal credit line to assign to the cluster.

Claims

1. A method for assigning a credit line to a credit card application, the method comprising the steps of:

receiving a plurality of credit card applications each having application information from an applicant;

retrieving credit bureau information for each of the plurality of credit card applications;

for each of the plurality of credit card applications, modeling applicant behavior by determining for each application a predicted account balance to be maintained by the applicant, a predicted revenue to be generated for a credit issuer by the applicant, a predicted amount of purchases to be made by the applicant, and a unit loss rate to predict a probability of default by the applicant, based at least in part on application information and credit bureau information;

generating an observation value for each credit card application based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate, and assigning each credit card application to a cluster based at least in part on the observation value for each credit card application;

deriving one or more financial measures for each cluster based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate; and

assigning a credit line to each cluster based at least in part on the one or more financial measures, wherein the credit line is assigned to each credit card application in a given cluster.

3. The method of claim 1, wherein the step of clustering is performed using a cluster analysis technique.

6. The method of claim 1, wherein the step of assigning includes selecting one or more objectives and one or more constraints from the one or more financial measures.

7. The method of claim 6, wherein the step of assigning the credit line includes maximizing the one or more objectives subject to the one or more constraints.

8. The method of claim 7, wherein the step of assigning the credit line includes using a branch bound algorithm.

9. The method of claim 1, further comprising the step of notifying an applicant of a credit line assignment.

2. (canceled)

4. (canceled)

5. (canceled)

10-18. -18. (canceled)

19. A computer-implemented method for assigning a credit line to a credit card application, the method comprising the steps of:

receiving a plurality of credit card applications each having application information from an applicant;

retrieving credit bureau information for each of the plurality of credit card applications;

for each of the plurality of credit card applications, modeling applicant behavior by determining for each application a predicted account balance to be maintained by the applicant, a predicted revenue for a credit issuer to be generated by the applicant, a predicted amount of purchases to be made by the applicant, and a unit loss rate to predict a probability of default by the applicant, based at least in part on application information and credit bureau information;

generating an observation value for each credit card application based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate, and assigning each credit card application to a cluster based at least in part on the observation value for each credit card application;

deriving one or more financial measures for each cluster based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate; and

assigning a credit line to each cluster based at least in part on the one or more financial measures, wherein the credit line is assigned to each credit card application in a given cluster.

21. The method of claim 19, wherein the step of clustering is performed using a cluster analysis technique.

24. The method of claim 19, wherein the step of assigning the credit line includes selecting one or more objectives and one or more constraints from the one or more financial measures.

25. The method of claim 24, wherein the step of assigning the credit line includes maximizing the one or more objectives subject to the one or more constraints.

20. (canceled)

22. (canceled)

23. (canceled)

26. A computer-readable storage medium storing computer code for implementing a method of assigning a credit line to a credit card application, wherein the computer code comprises:

code for receiving a plurality of credit card applications each having application information from an applicant;

code for retrieving credit bureau information for each of the plurality of credit card applications;

code for, for each of the plurality of credit card applications, modeling applicant behavior by determining for each application a predicted account balance to be maintained by the applicant, a predicted revenue to be generated for a credit issuer by the applicant, a predicted amount of purchases to be made by the applicant, and a unit loss rate to predict a probability of default by the applicant, based at least in part on application information and credit bureau information;

code for generating an observation value for each credit card application based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate, and assigning each credit card application to a cluster based at least in part on the observation value for each credit card application;

code for deriving one or more financial measures for each cluster based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate; and

code for assigning a credit line to each cluster based at least in part on the one or more financial measures, wherein the credit line is assigned to each credit card application in a given cluster.

27. A computer-implemented method for assigning a credit line to a credit card application, the method comprising the steps of:

receiving a plurality of credit card applications each having application information;

retrieving credit bureau information for each of the plurality of credit card applications;

for each credit card application, modeling applicant behavior with respect to a credit line assignment by determining for each application a predicted account balance to be maintained by the applicant, a predicted revenue to be generated for a credit issuer by the applicant, a predicted amount of purchases to be made by the applicant, and a unit loss rate to predict a probability of default by the applicant, based at least in part on application information and credit bureau information;

clustering the plurality of credit card applications into one or more clusters using a cluster analysis technique to generate an observation value for each credit card application based at least in part on predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate and to respectively assign each credit card application to a cluster based at least in part on a corresponding observation value for each credit card application;

deriving one or more financial measures for each cluster based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate; and

assigning a credit line to each cluster by selecting one or more objectives and one or more constraints from the one or more financial measures, and maximizing the one or more objectives subject to the one or more constraints, wherein the credit line is assigned to each credit card application in a given cluster.

28. A system for assigning a credit line to a credit card application, the system comprising:

an application processing module communicatively connected to a credit card application source, wherein the application processing module receives a plurality of credit card applications each having application information and retrieves credit bureau information for each of the plurality of credit card applications;

a behavior modeling module communicatively connected to the application processing module, wherein for each credit card application, the behavior modeling module models applicant behavior with respect to a credit line assignment by determining for each application a predicted account balance to be maintained by an applicant, a predicted revenue to be generated for the credit issuer by the applicant, a predicted amount of purchases to be made by the applicant, and a unit loss rate to predict a probability of default by the applicant, based at least in part on application information and credit bureau information;

a clustering module communicatively connected to the behavior modeling module, wherein the clustering module clusters the plurality of credit card applications into one or more clusters using a cluster analysis technique to generate an observation value for each credit card application based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate and to assign each credit card application respectively to a cluster based at least in part on a corresponding observation value for each credit card application;

a forecasting financial measures module communicatively connected to the clustering module, wherein the forecasting financial measures module derives one or more financial measures for each cluster based at least in part on the predicted account balance, the predicted revenue, the predicted amount of purchases, and the unit loss rate; and

a credit line assignment module communicatively connected to the forecasting financial measures module, wherein the credit line assignment module assigns a credit line to each cluster based at least in part on the one or more financial measures, wherein the credit line is assigned to each credit card application in a given cluster.

29. The system according to claim 28, wherein the credit line assignment module selects one or more objectives and one or more constraints from the one or more financial measures.

30. The system according to claim 29, wherein the credit line assignment module maximizes the one or more objectives subject to the one or more constraints.

31. The system according to claim 30, wherein the credit line assignment module maximizes the one or more objectives subject to the one or more constraints using a branch bound algorithm.

32. The system according to claim 28, wherein the credit line assignment module notifies an applicant of a credit line assignment.