Methods and system for assessing loss severity for commercial loans

• US 20070055595A1
• Filed: 09/06/2005
• Published: 03/08/2007
• Est. Priority Date: 09/06/2005
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: GE Corporate Financial Services, Inc. (General Electric Company)
• Inventors: Ruia, Sundeep, Keyes, Tim, Cole, Mary
• Granted Patent: US 8,065,214 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A method for predicting expected and unexpected loss outcomes for a portfolio of loans is provided. The loans are issued by a lender to a plurality of borrowers. The method includes recording key account and risk attributes for a historical portfolio of loans, recording actual default and loss information for each borrower included within the historical portfolio of loans, and comparing the key account and risk attributes with the actual default and loss information over a period of time. The method also includes selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, building a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, and predicting the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans.

Claims

1. A method for predicting expected and unexpected loss outcomes for a portfolio of loans using a computer system coupled to a database, the loans issued by a lender to a plurality of borrowers, said method comprising the steps of:

recording key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;

recording actual default and loss information in the database for each borrower included within the historical portfolio of loans;

comparing the key account and risk attributes with the actual default and loss information over a predetermined period of time, the comparison is performed by the computer;

selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;

building a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, the regression tree based model is built using the computer system; and

predicting the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender.

2. A method according to claim 1 wherein recording key account and risk attributes in the database for a historical portfolio of loans further comprises:

recording variations in the key account and risk attributes in the database for the historical portfolio of loans over a predetermined period of time;

comparing the key account and risk attributes, and the variations in the key account and risk attributes with the actual default and loss information over a predetermined period of time; and

selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the attributes and loss information comparison.

3. A method according to claim 1 wherein recording key account and risk attributes in the database for a historical portfolio of loans further comprises recording key account and risk attributes including at least one of the following financial measures for the corresponding borrower:

sales and EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization), debt service coverage, liquidation coverage, senior debt multiple, capital structure, company leverage, default likelihood, industry sector, exposure and credit line, and macro-economic factors such as employment and interest rates.

4. A method according to claim 1 wherein predicting the expected and unexpected loss outcomes for a second portfolio of loans further comprises performing a Monte Carlo simulation analysis for the second portfolio of loans using model structures and assumptions made in building the regression tree based model from the historical portfolio of loans.

5. A method according to claim 1 wherein building a regression tree based model further comprises dividing the historical portfolio of loans into segments of expected and unexpected losses, wherein expected loss for a segment of loans is equal to a probability of default (PD) multiplied by a mean of a loss-given-default (mean LGD) for the segment, and wherein the unexpected loss is a function of a mean of the PD, a standard deviation of the PD, the mean LGD, and a standard deviation of LGD for the segment.

6. A method according to claim 1 wherein building a regression tree based model further comprises:

building the regression tree based model based on the historical portfolio of loans;

maintaining a hold-out sample of loans from the historical portfolio of loans, the hold-out sample includes a portion of loans from the historical portfolio of loans not used in building the regression tree based model; and

using the hold-out sample of loans to test predictive accuracy of the built regression tree based model.

7. A method according to claim 1 wherein predicting the expected and unexpected loss outcomes for a second portfolio of loans further comprises:

calculating an economic capital for the second portfolio of loans based on the predicted expected and unexpected loss outcomes;

recording the calculated economic capital on the balance sheet of the lender; and

using the calculated economic capital for at least one of pricing potential future loans issued by the lender, and for account and portfolio management optimization by the lender.

8. A method according to claim 7 wherein using the calculated economic capital for at least one of pricing potential future loans issued by the lender further comprises calculating expected returns for each potential future loan as a function of future transaction cash flows.

9. A method according to claim 1 wherein predicting the expected and unexpected loss outcomes for a second portfolio of loans further comprises:

generating default likelihood data for each borrower included within the second portfolio of loans;

combining the predicted expected and unexpected loss outcomes for each borrower included with the second portfolio of loans with the corresponding default likelihood data; and

generating a matrix having dimensions for each borrower that include a probability of default (PD) by a mean loss-given-default (mean LGD).

10. A network-based system for predicting expected and unexpected loss outcomes for a portfolio of loans issued by a lender to a plurality of borrowers, said system comprising:

a client system comprising a browser;

a centralized database for storing information; and

a server system configured to be coupled to said client system and said database, said server further configured to;

record key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss,

record actual default and loss information in the database for each borrower included within the historical portfolio of loans,

compare the key account and risk attributes with the actual default and loss information over a predetermined period of time,

select a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss,

build a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, and

predict the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender.

11. A system according to claim 10 wherein said server system further configured to:

record variations in the key account and risk attributes in the database for the historical portfolio of loans over a predetermined period of time,

compare the key account and risk attributes, and the variations in the key account and risk attributes with the actual default and loss information over a predetermined period of time, and

select a sample of loans from the historical portfolio of loans to determine loss drivers based on the attributes and loss information comparison.

12. A system according to claim 10 wherein the key account and risk attributes comprise at least one of the following financial measures for the corresponding borrower:

sales and EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization), debt service coverage, liquidation coverage, senior debt multiple, capital structure, company leverage, default likelihood, industry sector, exposure and credit line, and macro-economic factors such as employment and interest rates.

13. A system according to claim 10 wherein said server system further configured to perform a Monte Carlo simulation analysis for the second portfolio of loans using model structures and assumptions made in building the regression tree based model from the historical portfolio of loans, the simulation analysis is used for predicting the expected and unexpected loss outcomes for the second portfolio of loans.

14. A system according to claim 10 wherein said server system further configured to build a regression tree based model by dividing the historical portfolio of loans into segments of expected and unexpected losses, wherein expected loss for a segment of loans is equal to a probability of default (PD) multiplied by a mean of a loss-given-default (mean LGD) for the segment, and wherein the unexpected loss is a function of a mean of the PD, a standard deviation of the PD, the mean LGD, and a standard deviation of LGD for the segment.

15. A system according to claim 10 wherein said server system further configured to:

build the regression tree based model based on the historical portfolio of loans,

maintain a hold-out sample of loans from the historical portfolio of loans, the hold-out sample includes a portion of loans from the historical portfolio of loans not used in building the regression tree based model, and

using the hold-out sample of loans to test predictive accuracy of the built regression tree based model.

16. A system according to claim 10 wherein said server system further configured to:

calculate an economic capital for the second portfolio of loans based on the predicted expected and unexpected loss outcomes,

record the calculated economic capital on the balance sheet of the lender, and

use the calculated economic capital for at least one of pricing potential future loans issued by the lender, and for account and portfolio management optimization by the lender.

17. A system according to claim 16 wherein said server system further configured to calculate expected returns for each potential future loan as a function of future transaction cash flows.

18. A system according to claim 10 wherein said server system further configured to:

generate default likelihood data for each borrower included within the second portfolio of loans,

combine the predicted expected and unexpected loss outcomes for each borrower included with the second portfolio of loans with the corresponding default likelihood data, and

generate a matrix having dimensions for each borrower that include a probability of default (PD) by a mean loss-given-default (mean LGD).

19. A computer for predicting expected and unexpected loss outcomes for a portfolio of loans issued by a lender to a plurality of borrowers, said computer in communication with a database for storing information relating to each loan and each borrower, said computer programmed to:

record key account and risk attributes in the database for a historical portfolio of loans issued by the lender, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;

record actual default and loss information in the database for each borrower included within the historical portfolio of loans;

compare the key account and risk attributes with the actual default and loss information over a predetermined period of time;

select a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;

build a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans; and

predict the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender.

20. A computer according to claim 19 wherein said computer is further programmed to:

record variations in the key account and risk attributes in the database for the historical portfolio of loans over a predetermined period of time;

compare the key account and risk attributes, and the variations in the key account and risk attributes with the actual default and loss information over a predetermined period of time; and

select a sample of loans from the historical portfolio of loans to determine loss drivers based on the attributes and loss information comparison.

21. A computer according to claim 19 wherein said computer is further programmed to perform a Monte Carlo simulation analysis for the second portfolio of loans using model structures and assumptions made in building the regression tree based model from the historical portfolio of loans, the simulation analysis is used for predicting the expected and unexpected loss outcomes for the second portfolio of loans.

22. A computer according to claim 19 wherein said computer is further programmed to build a regression tree based model by dividing the historical portfolio of loans into segments of expected and unexpected losses, wherein expected loss for a segment of loans is equal to a probability of default (PD) multiplied by a mean of a loss-given-default (mean LGD) for the segment, and wherein the unexpected loss is a function of a mean of the PD, a standard deviation of the PD, the mean LGD, and a standard deviation of LGD for the segment.

23. A computer according to claim 19 wherein said computer is further programmed to:

calculate an economic capital for the second portfolio of loans based on the predicted expected and unexpected loss outcomes;

record the calculated economic capital on the balance sheet of the lender; and

use the calculated economic capital for at least one of pricing potential future loans issued by the lender, and for account and portfolio management optimization by the lender.

24. A computer according to claim 19 wherein said computer is further programmed to:

generate default likelihood data for each borrower included within the second portfolio of loans;

combine the predicted expected and unexpected loss outcomes for each borrower included with the second portfolio of loans with the corresponding default likelihood data; and

generate a matrix having dimensions for each borrower that include a probability of default (PD) by a mean loss-given-default (mean LGD).

25. A computer program embodied on a computer readable medium for predicting expected and unexpected loss outcomes for a portfolio of loans issued by a lender to a plurality of borrowers, said program comprising at least one code segment that prompts a user to input key account and risk attributes for a historical portfolio of loans issued by the lender and then:

records the key account and risk attributes in a database, the historical portfolio of loans includes loans issued to borrowers that have experienced a financial default with the lender and an associated economic loss;

records actual default and loss information in the database for each borrower included within the historical portfolio of loans;

compares the key account and risk attributes with the actual default and loss information over a predetermined period of time;

selects a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, the loss drivers include the key account and risk attributes predictive of a loss;

builds a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans; and

predicts the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans, the second portfolio of loans includes borrowers that have not experienced a default with the lender.

26. A computer program in accordance with claim 25 further comprising at least one code segment that:

records variations in the key account and risk attributes in the database for the historical portfolio of loans over a predetermined period of time;

compares the key account and risk attributes, and the variations in the key account and risk attributes with the actual default and loss information over a predetermined period of time; and

selects a sample of loans from the historical portfolio of loans to determine loss drivers based on the attributes and loss information comparison.

27. A computer program in accordance with claim 25 further comprising at least one code segment that performs a Monte Carlo simulation analysis for the second portfolio of loans using model structures and assumptions made in building the regression tree based model from the historical portfolio of loans, the simulation analysis is used for predicting the expected and unexpected loss outcomes for the second portfolio of loans.

28. A computer program in accordance with claim 25 further comprising at least one code segment that builds a regression tree based model by dividing the historical portfolio of loans into segments of expected and unexpected losses, wherein expected loss for a segment of loans is equal to a probability of default (PD) multiplied by a mean of a loss-given-default (mean LGD) for the segment, and wherein the unexpected loss is a function of a mean of the PD, a standard deviation of the PD, the mean LGD, and a standard deviation of LGD for the segment.

29. A computer program in accordance with claim 25 further comprising at least one code segment that:

calculates an economic capital for the second portfolio of loans based on the predicted expected and unexpected loss outcomes;

records the calculated economic capital on the balance sheet of the lender; and

uses the calculated economic capital for at least one of pricing potential future loans issued by the lender, and for account and portfolio management optimization by the lender.

30. A computer program in accordance with claim 25 further comprising at least one code segment that:

generates default likelihood data for each borrower included within the second portfolio of loans;

combines the predicted expected and unexpected loss outcomes for each borrower included with the second portfolio of loans with the corresponding default likelihood data; and

generates a matrix having dimensions for each borrower that include a probability of default (PD) by a mean loss-given-default (mean LGD).