Systems and methods to facilitate analysis of commercial credit customers

• US 20030120586A1
• Filed: 12/21/2001
• Published: 06/26/2003
• Est. Priority Date: 12/21/2001
• Status: Abandoned Application

• Current Assignee: General Electric Capital Corporation (General Electric Company)
• Original Assignee: General Electric Capital Corporation (General Electric Company)
• Inventors: Litty, Charles J.
• Application Number:
  US10/026,104
• Publication Number:
  US 20030120586A1
• 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

Systems and methods are provided to facilitate analysis of commercial credit customers. According to one embodiment, customer information associated with a commercial credit customer is determined, at least some of the customer information being associated with a plurality of commercial credit accounts. Risk information associated with the customer is then generated by applying at least one of a plurality of risk models to the customer information. A list of high risk customers may then be created based on the risk information.

Claims

1. A method for facilitating analysis of a commercial credit customer, comprising:

determining customer information associated with the commercial credit customer, at least some of the customer information being associated with a plurality of commercial credit accounts; and

generating risk information associated with the customer by applying at least one of a plurality of risk models to the customer information.

2. The method of claim 1, wherein said determining and generating are performed for a plurality of customers, and further comprising:

generating a list of high risk customers based on risk information.

3. The method of claim 2, further comprising:

periodically transmitting information associated with the list to a risk manager via a communication network.

4. The method of claim 1, wherein the customer information includes at least one of:

(i) a business segment, (ii) a company type, and (iii) a product type.

5. The method of claim 1, wherein the customer information includes at least one of:

(i) a customer characteristic, (ii) a payment history, (iii) a loss history, (iv) a delinquency status, and (v) an aggregate customer account size.

6. The method of claim 1, wherein the customer information includes at least one of:

(i) an account characteristic, (ii) a collateral type, (iii) a delinquency status, (iv) an original account size, (v) an account duration, (vi) a total balance, (vii) a maximum total balance within a pre-determined period of time, (viii) an original equipment cost, (ix) a security deposit, (x) payment timing information, and (xi) payment amount information.

7. The method of claim 1, wherein the risk information comprises at least one of:

(i) a risk score, and (ii) a risk category.

8. The method of claim 1, further comprising:

calculating a risk score based on the risk information, economic information, and deal information.

9. The method of claim 1, wherein the risk information is further based on information received from at least one third-party.

10. The method of claim 9, wherein the received information is associated with at least one of:

(i) a customer, (ii) a customer category, and (iii) a plurality of customers.

11. The method of claim 1, wherein the plurality of risk models were created based on historical commercial credit information.

12. The method of claim 11, wherein the plurality of risk models were further created based on at least one of:

(i) a segmentation analysis, (ii) a univariate analysis, and (iii) multivariate analysis.

13. The method of claim 1, further comprising:

selecting an appropriate risk model to apply to the customer information.

14. The method of claim 13, wherein said selecting is based on the customer information.

15. An apparatus, comprising:

a processor; and

a storage device in communication with said processor and storing instructions adapted to be executed by said processor to;

determine customer information associated with a commercial credit customer, at least some of the customer information being associated with a plurality of commercial credit accounts, and

generate risk information associated with the customer by applying at least one of a plurality of risk models to the customer information.

16. The apparatus of claim 15, wherein said storage device further stores at least one of:

(i) a customer database, (ii) an account database, (iii) a risk model database, and (iv) an analysis database.

17. The apparatus of claim 15, further comprising:

a communication device coupled to said processor and adapted to communicate with at least one of;

(i) an information device, (ii) an accounts receivable system, (iii) a third-party service, and (iv) a risk manager device.

18. A medium storing instructions adapted to be executed by a processor to perform a method for facilitating analysis of a commercial credit customer, said method comprising:

determining customer information associated with the commercial credit customer, at least some of the customer information being associated with a plurality of commercial credit accounts; and

generating risk information associated with the customer by applying at least one of a plurality of risk models to the customer information.

19. A method of facilitating commercial credit customer analysis, comprising:

identifying a set of historical customers, each historical customer being associated with at least one commercial credit account;

performing a segmentation analysis to determine customer segments;

identifying potential variables;

performing univariate analysis on the potential variables to identify potentially predictive variables;

performing multivariate analysis on the potentially predictive variables to select most predictive variables;

establishing risk models using the most predictive variables for each customer segment; and

for a plurality of active customers, periodically;

retrieving internal account data associated with the active customers,

receiving external customer data associated with the active customers,

identifying a subset of the active customers based on the risk models, internal account data, and external customer data, and

transmitting a notification associated with the subset of active customers to at least one risk manager via a communication network.