INSURANCE CLAIM FORECASTING SYSTEM

• US 20090048877A1
• Filed: 06/24/2008
• Published: 02/19/2009
• Est. Priority Date: 11/15/2000
• Status: Abandoned Application

• Current Assignee: Gregory S. Binns, Mark Stuart Blumberg
• Original Assignee: Gregory S. Binns, Mark Stuart Blumberg
• Inventors: Binns, Gregory S., Blumberg, Mark Stuart
• Application Number:
  US12/145,281
• Publication Number:
  US 20090048877A1
• Time in Patent Office: Days
• US Class Current: 705/4
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A computer-implemented process of developing a person-level cost model for forecasting future costs attributable to claims from members of a book of business, where person-level data are available for a substantial portion of the members of the book of business for an actual underwriting period, and the forecast of interest is for a policy period is disclosed. The process uses development universe data comprising person-level enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals. The process also provides at least one claim-based risk factor for each historical base period claim based on the claim code associated with the health care claim and provides at least one enrollment-based risk factor based on the enrollment data. The process also develops a cost forecasting model by capturing the predictive ability of the main effects and interactions of claim based risk factors and enrollment-based risk factors, with the development universe data through the application of an interaction capturing technique to the development universe data.

Claims

1. A computer-implemented process of developing a person-level cost model for forecasting future costs attributable to claims from members of a book of business, where person-level data regarding actual base period health care claims are available for a substantial portion of the members of the book of business for an actual underwriting period, and the forecast of interest (i.e., future claim amount) is for an actual policy period which can be, but is not necessarily contiguous with the actual underwriting period, comprising the steps of:

providing development universe data comprising person-level enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals, where the person-level data on a health care claim comprises at least a claim code and a claim amount;

providing at least one claim-based risk factor for each historical base period claim based on the claim code associated with the health care claim and providing at least one enrollment-based risk factor based on the enrollment data; and

developing a cost forecasting model by capturing the predictive ability of the main effects and interactions of claim based risk factors and enrollment-based risk factors, with the development universe data through the application of an interaction capturing technique to the development universe data.

2. The computer-implemented process of claim 1, wherein the interaction capturing technique is selected from the group consisting of median regression tree techniques, least square regression tree techniques, rule induction techniques, ordinary least squares regression techniques, median regression techniques, robust regression techniques, genetic algorithms, rule induction, clustering techniques and neural network techniques.

3. The computer implemented process of claim 1 wherein the person-level next period cost forecasts are adjusted by modifying the extant cost forecast by the expected cost trend.

4. The computer implemented process of claim 1 wherein the datum from the claims used as predictors consist essentially of the claim- and enrollment-based risk factors and the claim amount is a standardized cost of services provided and the model is used to allocate prospective payments to health care providers.

5. The computer implemented process of claim 1 wherein the data used from the claims data consist essentially of the claim code and selected mandatory procedures and the claim amount is a standardized cost of services provided during the same time period as the base period and the model is used to evaluate the efficiency of health care providers.

6. The computer implemented process of claim 1, further comprising a computer implemented process of forecasting future claim amounts attributable to claims from members of a book of business for an actual policy period, wherein the model development universe comprises data from the members of a book of business to be insured, further comprising:

applying the cost-forecasting model to the actual underwriting period person-level data of each of the members of the book of business to generate a person-level actual policy period cost forecast for each member of the book of business; and

producing a group-level forecast for the actual underwriting period from the person-level forecasts of each member of the group by totaling the person-level actual policy period cost forecasts for the group for the policy period.

7. The computer implemented process of claim 6, comprising in addition the step of:

setting insurance reserves based on group-level forecast for the actual policy period, wherein the policy period is a reserving period for claims that have not occurred or that have occurred but not been reported.

8. The computer implemented process of claim 6, wherein claim amounts are a mix of fee for service payments and capitation payments so that the base and underwriting periods risk factors are appended to include dummy variables for the presence of capitation payments by provider type and the cost estimate in the next and policy periods is the fee for service cost that must be supplemented with the expected capitation payments.

9. The computer implemented process of claim 6, wherein the cost forecast is produced for first-dollar health insurance.

10. The computer implemented process of claim 6, wherein the cost forecast is produced for specific plus aggregate stop loss health insurance.

11. The computer implemented process of claim 10, wherein the cost forecast produced is for aggregate-only stop loss health insurance.

12. The computer implemented process of claim 10, wherein the cost forecast produced is for specific stop loss health insurance.

13. The computer implemented process of claim 1, wherein each of the diagnosis and CPT based risk factors is independent of the sequence in time of the other diagnosis and CPT based risk factors.

14. The computer implemented process of claim 1, wherein the providing of risk factors for the health care claim data is substantially free of human expert interaction.

15. The computer implemented process of claim 1, wherein capturing the predictive ability of the main effects and interactions of claim based risk factors and enrollment-based risk factors is substantially free of human expert interaction.

16. The computer implemented process of claim 1, comprising in addition the step of:

setting medical insurance reserves through application of the health care cost forecasting model, wherein the next period is a reserving period for claim amounts that have not occurred or that have occurred but not been reported.

17. The computer implemented process of claim 1 for forecasting short term disability (STD) costs wherein a dependent measure for generating the cost forecasting model is the number of STD days in the policy period and is weighted by the expected cost per day for the STD to produce the person-level forecast STD costs and summed across the group to produce the group'"'"'s forecast STD cost.

18. The computer implemented process of claim 1, for forecasting a probability of long term disability (LTD) claims wherein a dependent measure for generating the cost forecasting model is the probability of a LTD claim in the policy period where the probability is weighted by the net present value of the LTD claim amount and comprises in addition producing person-level expected LTD costs and summing person-level expected LTD costs across the group to produce a group'"'"'s expected LTD cost.

19. The computer implemented process of claim 1 for forecasting group term life insurance costs wherein a dependent measure for generating the forecasting model is the expected probability of death weighted by the amount of life insurance to produce the person-level expected term life insurance cost which is summed across the group to produce the group'"'"'s expected term life insurance cost.

20. The computer implemented process of claim 1, wherein claim amounts are a mix of fee for service payments and capitation payments so that the base and underwriting periods risk factors are appended to include dummy variables for the presence of capitation payments by provider type.

21. A computer-implemented process of developing a hybrid person-level health care claim cost forecasting model for forecasting future medical costs attributable to health care claims from members of a book of business, where person-level data are available for a substantial portion of the members of the book of business, comprising the steps of:

providing development universe data comprising person-level data for a statistically meaningful number of individuals, the person-level data comprising continuous variable data and categorical variable data;

processing first the continuous variable data for each individual with a continuous processing technique that captures the predictive ability of main effects and interactions of continuous variables to generate a person-level continuous variable model; and

processing the categorical variable data for each individual including the output from the continuous processing technique with a categorical processing technique that captures the predictive ability of main effects and interactions of categorical variables to generate a person-level categorical variable model;

wherein the person-level continuous variable model and person-level categorical variable model together comprise a hybrid person-level health care claim amount forecasting model.

22. The computer-implemented process of claim 21, wherein the continuous variable data comprises data selected from the group consisting of age, length of prior enrollment, historical claim amounts and transformations and trends in the person level claim amounts.

23. The computer-implemented process of claim 21, wherein the categorical variable data comprises data selected from the group consisting of clinical risk factors, provider type and site of care.

24. The computer-implemented process of claim 21, wherein the continuous processing technique is selected from the group consisting of regression techniques and neural network techniques.

25. The computer-implemented process of claim 21, wherein the categorical processing technique is selected from the group consisting of median regression tree techniques, least square regression tree techniques, rule induction techniques, and neural network techniques.

26. The computer-implemented process of claim 21, wherein the person-level data is available for a substantial portion of the members of the book of business for an actual underwriting period, and the claim amount of interest for forecasting purposes are during an actual policy period which can be, but is not necessarily contiguous with the actual underwriting period, and the development universe data comprises person-level data for each individual for a historical base period and a historical next period.

27. The computer-implemented process of claim 21, wherein the hybrid person-level health care claim cost forecasting model is used as an input into an interaction capturing technique that uses all of the risk factors that were meaningful in the hybrid person-level health care claim cost forecasting model to forecast future medical claim amounts.

28. A computer-implemented process of developing a claim amount forecasting model for use in forecasting the future claim amount for members of a book of business, where person-level data are available for a substantial portion of the members of the book of business for an actual base period, and the claim amount of interest for forecasting purposes is an actual next period which can be, but is not necessarily contiguous with the actual base period, comprising the steps of:

processing the base period data having claims to generate a having-claims claim amount forecasting model; and

processing the base period data without claims to generate a without-claims claim amount forecasting model,

wherein the having-claims cost forecasting model and the without-claims forecasting model comprise a claim amount forecasting model.

29. A computer-implemented process of developing a health care claim amount forecasting model for use in forecasting the future medical claim amount for members of a book of business, where person-level data are available for a substantial portion of the members of the book of business for an actual base period, and the claim amount of interest for forecasting purposes is an actual next period which can be, but is not necessarily contiguous with the actual base period, comprising the steps of:

providing development universe data comprising person-level data for a statistically meaningful plurality of individuals, wherein the person-level data for an individual comprises health care claims data for the individual and the data on a health care claim comprises at least a claim amount and a claim code;

Winsorizing the person-level data to yield inlier data and outlier data;

processing the inlier data to generate an inlier cost forecasting model; and

processing the outlier data to generate an outlier cost forecasting model;

wherein the combination of the results of the inlier and outlier cost forecasting models together produce a person-level claim amount forecast model.

30. The computer-implemented process of claim 29 further comprising:

Winsorizing the inlier data to yield inlier data having claims and inlier data without claims;

processing the inlier data having claims to generate an inlier-having-claims claim amount forecasting model; and

processing the inlier data without claims to generate an inlier-without-claims claim amount forecasting model,

wherein the inlier-having-claims cost forecasting model and the inlier-without-claims forecasting model comprise an inlier claim amount forecasting model.

31. A computer-implemented process of forecasting a claim amount attributable to claims from members of a book of business during an actual policy period, comprising the steps of:

providing person-level data, comprising enrollment data for members of a book of business to be insured for an actual underwriting period that can be, but is not necessarily, contiguous with the actual policy period;

providing a model development universe of person-level data, comprising enrollment data from the historical base period and historical next period heath care claims data for a statistically meaningful number of individuals;

providing enrollment-based risk factors for each historical base period and providing next period claim amounts;

developing a health care cost-forecasting model for the enrollment data by capturing the predictive ability of main effects and interactions of enrollment-based risk factors through the application of an interaction capturing techniques to the model development universe;

applying the health care cost-forecasting model to the person-level underwriting period enrollment data of each of the members of the book of business to generate a person-level expected cost forecast for the policy period for each member of the book of business; and

producing a group-level forecast for the expected cost of the policy period from the person-level forecasts of each person of the group by totaling the person-level expected cost forecasts for the actual policy period.

32. A computer-implemented process of forecasting costs attributable to claims from members of a book of business during an actual policy period, comprising the steps of:

providing person-level data, comprising enrollment data and actual underwriting period health care claims data, for members of a book of business, where the person-level data on a health care claim comprises at least a claim amount and a claim code and the actual underwriting period can be, but is not necessarily, contiguous with the actual policy period;

providing a model development universe of person-level data, comprising enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals, where the person-level data on a base period health care claim includes at least a claim amount and a claim code;

providing claim-based risk factors for each historical base period based on the claim code associated with the health care claim and providing at least one enrollment risk factor based on the enrollment data;

developing a cost-forecasting model by capturing the predictive ability of main effects and interactions of risk factors through the application of an interaction capturing technique to the model development universe;

applying the cost-forecasting model to the person-level data of each of the individuals or members of a group to generate a person-level actual policy period expected cost forecast for each member of the group; and

producing a group-level forecast for the actual policy period from the person-level forecasts of each individual or member of the group by totaling the person-level cost forecasts for the actual policy period.

33. The computer implemented process of claim 32, comprising in addition the step of:

setting claim amount reserves based on the individual or group-level forecast, wherein the next period is a reserving period for claims that have not occurred or that have occurred but not been reported.

34. The computer implemented process of claim 32 for forecasting short term disability costs wherein the interaction capturing technique uses a dependent measure from the next period and policy period comprising the number of STD days in the policy period and weights the dependent measure by the expected cost per day for the STD to produce the person-level expected STD costs and summed across the group to produce the group'"'"'s expected STD cost.

35. The computer implemented process of claim 32, for forecasting a probability of long term disability (LTD) claims wherein a dependent measure for generating the cost forecasting model is the probability of a LTD claim in the policy period where the probability is weighted by the net present value of the LTD and applying the cost forecasting model to the person-level data produces person-level expected LTD costs wherein summing the person-level expected LTD costs across the group to produce a group'"'"'s expected LTD cost for an actual policy period.

36. The computer implemented process of claim 32, wherein the cost forecast is produced for first-dollar health insurance.

37. The computer implemented process of claim 32, wherein the cost forecast is produced for specific plus aggregate stop loss health insurance.

38. The computer implemented process of claim 32, wherein the cost forecast produced is for aggregate-only stop loss health insurance.

39. The computer implemented process of claim 32, wherein the cost forecast produced is for specific stop loss health insurance.

40. The computer implemented process of claim 32 for forecasting group term life insurance costs wherein a dependent measure for generating the cost forecasting model is the expected probability of death weighted by the amount of life insurance to produce the person-level expected term life insurance cost which is summed across the group to produce the group'"'"'s expected term life insurance cost.

41. The computer implemented process of claim 32, wherein claim amounts are a mix of fee for service payments and capitation payments so that the base and underwriting periods risk factors are appended to include dummy variables for the presence of capitation payments by provider type and the cost estimate in the next and policy periods is the fee for service cost that must be supplemented with the expected capitation payments.

42. The process of claim 32 further comprising developing group-level cost-forecasting model for groups in the book of business by capturing the predictive ability of main effects and interactions of group-level risk factors which include but are not limited to groups historical claim amounts, group-level sum of the person-level forecasts, SIC code or industry type, characteristics of the benefit plan design, geographic locale, and number of people and length of time covered by the insurance through the application of an interaction capturing technique to the model development universe of groups.

43. The computer implemented process of claim 42, comprising in addition the step of:

setting medical insurance reserves based on the group-level forecast, wherein the next period is a reserving period for claims that have not occurred or that have occurred but not been reported.

44. The computer implemented process of claim 42 for forecasting short term disability costs wherein the interaction capturing technique uses a group-level dependent measure of residual STD days at the group-level calculate forecast STD costs by weighting by the group'"'"'s expected STD cost per day.

45. The computer implemented process of claim 42, wherein medical claim amounts are a mix of fee for service payments and capitation payments so that the base and underwriting periods group-level risk factors are appended to include dummy variables for the presence of capitation payments by provider type and the cost estimate in the next and policy periods is the fee for service cost that must be supplemented with the expected capitation payments.

46. The process of claim 32 comprising in addition the steps of:

providing a provider type cost trend forecast adjustment to be utilized by at least one member of the group to be insured;

adjusting the person-level next period cost forecast for each member using the health care provider type with the provider type cost trend forecast adjustment.

52. A group insurance product comprising:

an identification of the types of benefits which are agreed to be provided by an insurer to or on behalf of members of a group, which will be incurred by members of said group during a future time period; and

a stated monetary insurance premium including a forecast of said benefits made in accordance with the process of claim 32, estimated costs of administering the insurance product, and optionally, an estimated profit,

whereby an insurer agrees to cover the identified benefits in exchange for the payment of the stated monetary insurance premium.

53. The group health insurance product of claim 52 for insuring short term disability costs wherein the interaction capturing technique uses a dependent measure from the next period and policy period comprising the number of STD days in the policy period and weights the dependent measure by the expected cost per day for the STD to produce the person-level expected STD costs and summed across the group to produce the group'"'"'s expected STD cost.

54. The group health insurance product of claim 52 for insuring long term disability (LTD) claims wherein a dependent measure for generating the claim amount forecasting model is the probability of a LTD claim in the policy period where the probability is weighted by the net present value of the LTD and applying the cost forecasting model to the person-level data produces person-level expected LTD costs wherein summing the person-level expected LTD costs across the group to produce a group'"'"'s expected LTD cost for an actual policy period.

55. The group health insurance product of claim 52, wherein the cost forecast is produced for first-dollar health insurance.

56. The group health insurance product of claim 52, wherein the cost forecast is produced for specific plus aggregate stop loss health insurance.

57. The group health insurance product of claim 52, wherein the cost forecast produced is for aggregate-only stop loss health insurance.

58. The group health insurance product of claim 52, wherein the cost forecast produced is for specific stop loss health insurance.

59. The group health insurance product of claim 52 for insuring group term life insurance costs wherein a dependent measure for generating the cost forecasting model is the expected probability of death weighted by the amount of life insurance to produce the person-level expected term life insurance cost.

60. The group health insurance product of claim 52, comprising a renewal product, wherein the model development universe comprises data from the members of a group in the book of business to be insured.

62. A method of pricing group insurance including a cost of future benefits according to the computer-implemented process of forecasting future medical costs attributable to claims from members of a group during an actual underwriting period of claim 32, comprising the additional steps of:

providing an expected amount of administrative costs allocable to providing health insurance coverage to the group;

providing a minimum acceptable expected profit;

totaling the group level cost forecast, expected amount of administrative costs, and minimum acceptable expected profit are to yield a total minimum price, and

providing a plurality of expected probabilities of retention for the group corresponding to a plurality of possible prices greater than or equal to the total minimum price, each possible price also having an expected profit that is the amount of the price over the group level cost forecast plus the expected amount of administrative costs; and

calculating a plurality of possible maximum profits by multiplying each of the plurality of possible profits by the corresponding expected probability of retention,

wherein the largest possible maximum profit, is used to price the group insurance.

63. A method of pricing group insurance of claim 62 for insuring short term disability costs wherein the interaction capturing technique uses a dependent measure from the next period and policy period comprising the number of STD days in the policy period and weights the dependent measure by the expected cost per day for the STD to produce the person-level expected STD costs and summed across the group to produce the group'"'"'s expected STD cost.

64. A method of pricing group insurance of claim 62 for insuring long term disability (LTD) claims wherein a dependent measure for generating the cost forecasting model is the probability of a LTD claim in the policy period where the probability is weighted by the net present value of the LTD and applying the cost forecasting model to the person-level data produces person-level expected LTD costs wherein summing the person-level expected LTD costs across the group to produce a group'"'"'s expected LTD cost for an actual policy period.

65. A method of pricing group insurance of claim 62, wherein the pricing is produced for first-dollar health insurance.

66. A method of pricing group insurance of claim 62, wherein the pricing is produced for stop loss health insurance.

67. A method of pricing group insurance of claim 62, wherein the pricing produced is for aggregate-only stop loss health insurance.

68. A method of pricing group insurance of claim 62, wherein the pricing produced is for specific stop loss health insurance.

69. A method of pricing group insurance of claim 62 for insuring group term life insurance costs wherein a dependent measure for generating the cost forecasting model is the expected probability of death weighted by the amount of life insurance to produce the person-level expected term life insurance cost.

70. A method of pricing group insurance of claim 62, comprising a renewal product, wherein the model development universe comprises data from the members of a group in the book of business to be insured.

47. An automated system for forecasting future costs attributable to claims from members of a book of business during an actual policy period comprising:

a central processing unit;

an insured person database, accessible by the processor, wherein the database comprises person-level enrollment data and actual underwriting period health care claims data, for members of a book of business to be insured, where the person-level data on a health care claim comprises at least a claim amount and a claim code;

a model development universe database, accessible by the processor, wherein the second database comprises model development universe of person-level data, comprising enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals, where the person-level data on the base period health care claim includes at least a claim amount and a claim code;

a risk factor encoder, accessible by the processor, wherein the risk factor encoder encodes claim-based risk factors for each historical base period based on the claim code associated with the health care claim and the risk factor encoder encodes at least one enrollment risk factor based on the enrollment data;

a model generator, accessible by the processor, that generates a cost-forecasting model by capturing the predictive capacity of the main effects and the interaction of the risk factors assigned by the risk factor encoder to forecast the historical next period of the model development universe data using the historical base period data;

a person-level cost generator that applies the cost-forecasting model to the person-level actual underwriting period health care claims data of each of the members of the book of business to generate a person-level actual policy period claim amount forecast for each member of the book of business; and

an actual policy period group-level cost forecast generator that totals the person-level actual next period forecasts for each member of the group to generate an actual policy period group-level cost forecast.

48. The system of claim 47 wherein the model generator captures the predictive ability of main effects and interactions of group-level risk factors which include but are not limited to groups historical claim amounts, group-level sum of the person-level forecasts, SIC code or industry type, characteristics of the benefit plan design, geographic locale, and the number of people and length of time covered by the insurance through the application of an interaction capturing technique to the model development universe of groups.

61. A method of reserving for the group health insurance product of claim 48, comprising in addition the step of:

setting insurance reserves based on the renewal group-level forecast for the actual underwriting period, wherein the next period is a reserving period for claims that have not occurred or that have occurred but not been reported.

49. A computer-implemented process of forecasting costs attributable to claims from members of a book of business during an actual policy period, comprising the steps of:

means for providing person-level data, comprising enrollment data and actual underwriting period health care claims data, for members of a book of business, where the person-level data on a health care claim comprises at least a claim amount and a claim code and the actual underwriting period can be, but is not necessarily, contiguous with the actual policy period;

means for providing a model development universe of person-level data, comprising enrollment data, historical base period health care claims data and historical next period claim amount data for a statistically meaningful number of individuals, where the person-level data on a base period health care claim includes at least a claim amount and a claim code;

means for providing claim-based risk factors for each historical base period based on the claim code associated with the health care claim and providing at least one enrollment risk factor based on the enrollment data;

means for developing a cost-forecasting model by capturing the predictive ability of main effects and interactions of risk factors through the application of an interaction capturing technique to the model development universe;

means for applying the cost-forecasting model to the person-level data of each of the individuals or members of a group to generate a person-level actual policy period expected cost forecast for each member of the group; and

means for producing a group-level forecast for the actual policy period from the person-level forecasts of each individual or member of the group by totaling the person-level cost forecasts for the actual policy period.

50. The system recited in claim 49 wherein the system further is automated such that when actual underwriting period data is provided the system automatically provides an actual policy period claim amount forecast.

51. The system recited in claim 49 for use by a client having data and an Internet client application, further comprising an Internet server application such that when the client provides actual underwriting period data to the Internet server application, the Internet server application automatically provides an actual policy period claim amount forecast.

71. A method of underwriting an insurance product comprising the steps of:

providing an identification of the coverage of the insurance product which identifies the conditions of payment under the product during a policy period;

providing person-level health care claim information comprising enrollment data, and base period and underwriting period claim data, the claim data comprising claim codes having associated claim costs;

capturing the predictive ability of the person-level health care claim information through the application of an interaction capturing technique; and

forecasting a predicted cost of the insurance product during the policy period based on the identification of the coverage of the insurance product and the captured predictive ability of the person-level health care claim information;

wherein each of diagnosis and CPT based risk factor is independent of the sequence in time of other diagnosis and CPT based risk factors.

72. The method of underwriting an insurance of claim 71, for insuring short term disability costs wherein the interaction capturing technique uses a dependent measure from the next period and policy period comprising the number of STD days in the policy period and weights the dependent measure by the expected cost per day for the STD to produce the person-level expected STD costs and summed across the group to produce the group'"'"'s expected STD cost.

73. The method of underwriting a insurance of claim 71, for insuring long term disability (LTD) claims wherein a dependent measure for generating the cost forecasting model is the probability of a LTD claim in the policy period where the probability is weighted by the net present value of the LTD and applying the cost forecasting model to the person-level data produces person-level expected LTD costs wherein summing the person-level expected LTD costs across the group to produce a group'"'"'s expected LTD cost for an actual policy period.

74. The method of underwriting a insurance of claim 71, wherein the cost forecast is produced for first-dollar health insurance.

75. The method of underwriting a insurance of claim 71, wherein the cost forecast is produced for stop loss health insurance.

76. The method of underwriting a insurance of claim 71 wherein the cost forecast produced is for aggregate-only stop loss health insurance.

77. The method of underwriting a insurance of claim 71 wherein the cost forecast produced is for specific stop loss health insurance.

78. The method of underwriting a insurance of claim 71 for insuring group term life insurance costs wherein a dependent measure for generating the cost forecasting model is the expected probability of death weighted by the amount of life insurance to produce the person-level expected term life insurance cost.

79. The method of underwriting a insurance of claim 71 comprising renewal underwriting, wherein the model development universe comprises data from the members of a group in the book of business to be insured.

80. The method of underwriting a insurance of claim 71 comprising in addition the step of:

setting insurance reserves based on the renewal group-level forecast for the actual underwriting period, wherein the next period is a reserving period for claims that have not occurred or that have occurred but not been reported.