Abstract: The development and use of a model to estimate disability insurance exposure for a group of individuals to be underwritten for a coverage period is disclosed. The development of the model includes the use of medical claim data having a plurality of medical claims made by a first model population having at least a respective plurality of first model population members and a disability insurance claim data having a plurality of disability insurance claims made by a second model population having at least a respective plurality of second model population members, which optionally may partially or wholly overlap with the first model population. A clinical medical condition classification system assigns corresponding medical condition causes to the disability insurance claims and the medical claims based on medical condition information.

Abstract: The development and use of a model to estimate disability insurance exposure for a group of individuals to be underwritten for a coverage period is disclosed. The development of the model includes the use of medical claim data having a plurality % of medical claims made by a first model population having at least a respective plurality of first model population members and a disability insurance claim data having a plurality of disability insurance claims made by a second model population having at least a respective plurality of second model population members, which optionally may partially or wholly overlap with the first model population. A clinical medical condition classification system assigns corresponding medical condition causes to the disability insurance claims and the medical claims based on medical condition information.

Abstract: A method of underwriting group life insurance for a policy period includes collecting medical claims data for the group to be underwritten, where each medical claim being related to a particular employee of the group. Morbidity categories are provided that categorize the medical claims in the medical claims data. A conditional probability model is developed and applied to the morbidity categories for each employee in the group using his medical claims, thereby calculating the expected conditional probability for each employee dying during the policy period. For each employee, an estimate of the expected life claim cost is estimated using an index of the life coverage to salary. Combining the expected conditional probability for each employee dying during the policy period with the estimate of the expected claim cost of death gives an estimate of the group's total life exposure.

Abstract: A method of model development for use in underwriting group life insurance for a policy period includes collecting medical claims data for the group to be underwritten, where each medical claim being related to a particular employee of the group. Morbidity categories are provided that categorize the medical claims in the medical claims data. A conditional probability model is developed and applied to the morbidity categories for each employee in the group using his medical claims, thereby calculating the expected conditional probability for each employee dying during the policy period. For each employee, an estimate of the expected life claim cost is estimated using an index of the life coverage to salary. Combining the expected conditional probability for each employee dying during the policy period with the estimate of the expected claim cost of death gives an estimate of the group's total life exposure.

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.

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.

Abstract: A method of underwriting group disability insurance for a policy period includes collecting medical claims data for the group to be underwritten, where each medical claim being related to a particular employee of the group. Morbidity categories are provided that categorize the medical claims in the medical claims data. A conditional probability model is developed and applied to the morbidity categories for each employee in the group using his medical claims, thereby calculating the expected conditional probability for each employee of incurring a disability during the policy period. A further statistical model of the estimated cost of the disability is developed and applied based on the employees' morbidity categories from the medical claims data. For each employee, an estimate of the expected cost of incurring a disability given their morbidity categories is derived from his medical claims data.