Abstract: A system for helping a particular policy holder of a long term care insurance policy stay independent during a next year includes an external data gatherer, a model builder and an intervention determiner. The external data gatherer gathers claim data, and observation data of medical data and self-sufficiency data about a plurality of policy holders. The model builder builds a predictive model from features based on the claim data and the observation data and determines which of the policy holders are unlikely to remain independent during the next year. The intervention determiner determines, for a particular policy holder, an intervention to improve a probability that the particular policy holder remain independent for another year.

Abstract: A system for population health management to enable policy holders to defer long term care for the near term includes an external data gatherer, a model builder and an intervention determiner. The external data gatherer gathers data about a plurality of policy holders from at least two of medical records, direct observation, third party data sources, and questionnaires about lifestyles, family and caregivers. The model builder builds a model from at least one of scientifically based research and the gathered data and determines which of the policy holders are likely to claim for long term care in the near term. The intervention determiner determines, for a particular policy holder, at least one scientifically based intervention to improve a probability that the particular policy holder remain at home for another year.

Abstract: A method has a model predicting which elderly people are likely to claim for long term care (LTC) within a year based on long term care insurance claims of the elderly people and on assessment data of some of the elderly people. The method includes receiving living environment data of a particular senior with similar characteristics to the elderly people, which includes mobile device patterns of social activity, physical activity, phone use, shopping, sleeping, and waking by the particular senior, from the living environment data, determining a single feature of the features of the model whose change most likely reduces a probability of filing a claim for LTC within a period of time, providing to the particular senior a single intervention related to the single feature, and comparing further patterns of the particular senior with the previous patterns to determine an extent to which the particular senior implemented the intervention.

Abstract: A tool for test-based updating of actuarial tables includes an analyzer, a statistical transformer and an actuarial analyzer. The analyzer analyzes the results of cognitive and physical tests performed on a candidate of a known age and generates cognitive and physical scores for the results. The statistical transformer utilizes the scores and the known age to generate a statistical shift and generates adjusting factors Fi for the actuarial tables from the statistical shift. The actuarial analyzer raises the mortality probabilities of the actuarial tables by a power of 1/Fi per table, thereby to produce personalized actuarial tables for the candidate.

Abstract: A block prediction tool for actuaries includes a parametric model definer, a parametric model-based predictor and a robustness checker. The parametric model definer enables an actuary to define an initial parametric model. The predictor generates a current model by finding parameters for the initial parametric model which provide a match to data of an insurance block and produces a prediction for the insurance block using the current model. The robustness checker checks a robustness of the current model with respect to stochastic noise, parametric noise and time stability individually and in combination and generates an overall risk-level for the insurance block therefrom.

Abstract: A pricing determiner for a reinsurance contract includes at least one database and a processor. The database stores block data of a block of policy holders and externally gathered data, a least a portion of which is related to the policy holders. The processor implements a reinsurance pricing determiner which includes a model builder, a probability function generator and a pricing determiner. The model builder predicts which policy holders will have an event on their policies and within what time frame and is operative on the block data and the externally gathered data. The probability function generator generates a probability function from the model, the block data, and the externally gathered data. The pricing determiner activates the probability function generator on different portions of the policy holders and generates from the resultant probability functions a price for the reinsurance bracketed within a price range.

Abstract: A system for health management for policy holders to reduce disability in a long term. The system includes an external data gatherer, a model builder, a filterer and a plan selector. The external data gatherer gathers data about a plurality of policy holders from at least two of: medical records, wearable sensors, questionnaires about lifestyles, direct observations by professionals, family and caregivers, and third-party data sources. The model builder builds a health outcome model on the gathered data to generate probabilities of health outcomes and uses previously determined impact coefficients generated to determine the need for long term care in a near term. The filterer filters the probabilities to generate risk factors for a particular policy holder. The plan selector combines the risk factors with the gathered data and current incentive options to generate a health improvement plan for the particular policy holder.

Abstract: A system for population health management to enable policy holders to defer long term care for the near term includes an external data gatherer, a model builder and an intervention determiner. The external data gatherer gathers data about a plurality of policy holders from at least two of medical records, direct observation, third party data sources, and questionnaires about lifestyles, family and caregivers. The model builder builds a model from at least one of scientifically based research and the gathered data and determines which of the policy holders are likely to claim for long term care in the near term. The intervention determiner determines, for a particular policy holder, at least one scientifically based intervention to improve a probability that the particular policy holder remain at home for another year.