System and method for rating and structuring bands of crop production insurance

• US 20030061075A1
• Filed: 05/17/2002
• Published: 03/27/2003
• Est. Priority Date: 05/17/2001
• Status: Abandoned Application

• Current Assignee: Converium Reinsurance (North America), Inc. (Berkshire Hathaway Incorporated)
• Original Assignee: Converium Reinsurance (North America), Inc. (Berkshire Hathaway Incorporated)
• Inventors: Zhang, Bin, Tran, James, Heckman, Roger, Besselman, Catherine, Fischer, Bill
• Application Number:
  US10/151,488
• Publication Number:
  US 20030061075A1
• Time in Patent Office: Days
• US Class Current: 705/4
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method of providing insurance coverage for a producer, the method comprising: receiving producer input data, identifying an insured value for an amount of production; and defining one or more bands of coverage for the producer based on the input data and the insured value, each band of coverage including an upper limit and a non-zero lower limit.

Claims

1. A method of calculating an insurance premium for a producer based on an insured value, the method comprising:

defining a band of coverage having an upper and a lower limit;

determining an expected yield for the band of coverage;

simulating an expected loss for the band of coverage based on the expected yield; and

calculating the insurance premium based on the expected loss and the insured value.

2. A method as in claim 1, further comprising:

calculating a band coverage in units of production per land area by subtracting the lower limit from the upper limit and multiplying the result thereof by the expected yield.

3. A method as in claim 2, further comprising:

calculating a pure rate for the band of coverage by dividing the expected loss by the band coverage.

4. A method as in claim 3, wherein the step of calculating the insurance premium comprises multiplying together the band coverage, the pure rate and the insured value.

5. A method as in claim 1, wherein the insurance premium is calculated by multiplying the expected loss by the insured value.

6. A method as in claim 1, wherein the insurance premiums are calculated for more than one band of coverage.

7. A method as in claim 1, wherein the insured value is selected by the producer.

8. A method as in claim 1, wherein the expected yield is determined by:

calculating a county trended yield using county crop yield data;

calculating a farm yield using data specific to the producer;

estimating the expected yield based on the county trended yield and the farm yield.

9. A method as in claim 8, wherein the expected yield is estimated using a weighted average of the county trended yield and the farm yield.

10. A method as in claim 1, wherein the band of coverage comprises up to 90% of the expected yield.

11. A method as in claim 9, wherein the county trended yield is calculated as:

Y_ct=f(T)+ε

_ct

wherein;

Y_ct is the county trended yield;

T is a year index;

f is a function form; and

ε

_ct is white-noise term for the county crop yield at year T.

12. A method as in claim 11, wherein the county trended yield is validated using a statistical significance test for one or more county and crop combinations.

13. A method as in claim 12, wherein the statistical significance test is a linear trending model.

14. A method as in claim 12, wherein the statistical significance test is measured at a 95% confidence level.

15. A method as in claim 11, wherein the farm yield is calculated as:

Y_ft=f(T)+ε

_ft

wherein;

Y_ft is the farm yield; and

ε

_ft is white-noise term for actual farm yield at year T.

16. A method as in claim 15, wherein the county trended yield is validated using a statistical significance test for one or more county and crop combinations.

17. A method as in claim 16, wherein the statistical significance test is a linear trending model.

18. A method as in claim 16, wherein the statistical significance test is measured at a 95% confidence level.

19. A method as in claim 18, wherein a zero trending is used when the 95% confidence level is not statistically significant.

20. A method as in claim 8, further comprising:

initiating a bootstrapping process so as to establish a farm yield forecast based on a statistical relationship between the county trended yield and the farm yield.

21. A method as in claim 1, wherein the expected loss is simulated by:

generating a plurality of farm yields based on a yield trigger; and

calculating the expected loss as an average payout over the band of coverage based on the plurality of farm yields.

22. A method as in claim 21, wherein the yield trigger is defined by the upper limit of the band of coverage multiplied by the expected yield and the lower limit of the band of coverage multiplied by the expected yield.

23. A method of providing insurance coverage for a producer, the method comprising:

receiving producer input data;

identifying an insured value for an amount of production; and

defining one or more bands of coverage for the producer based on the input data and the insured value, each band of coverage including an upper limit and a non-zero lower limit.

24. A method as in claim 23, wherein the upper limit is up to 90% of an expected yield.

25. A method as in claim 23, further comprising:

prompting the producer to select one of the one or more bands of coverage.

26. A method as in claim 25, further comprising:

providing an insurance policy for the producer for the selected band of coverage.

27. A method as in claim 25, further comprising:

calculating an insurance premium for each band of coverage based on the insured value.

28. A method as in claim 27, wherein the premium amount is calculated by:

determining an expected yield for the band of coverage;

simulating an expected loss for the band of coverage based on the expected yield; and

utilizing the expected loss and the insured value to calculate the insurance premium.

29. A method as in claim 23, wherein the producer input data comprises at least one of location information, crop identification, practice information, acreage, and actual production history data.