Description: This review summarizes the key features of a stochastic model that works with non-life insurance claim incurral, accrual and reporting. More specifically it provides an overview of the claim model components, classical parameter estimators, test statistics, and forecast point estimates and error variances. From Actuarial Research Clearing House 1991, VOL. 1.

Description: A mathematical framework for benefits and choices must be created, in order to model flexible benefit selection. This paper creates such a framework by defining benefit plans as reimbursement functions. These are then used with a defined choice function to calculate the cost deviation due to selection. From Actuarial Research Clearing House 1992, VOL. 1.

Description: This paper presents Bayesian models for analysis of claim lag data. The analysis of claim runoff patterns investigate the extraction of information on trend and seasonal patterns in incurred claims. This is investigated on the basis of individual group data, and also relative to a larger aggregation of pooled experience. The models are implemented based on Markov chain Monte Carlo methods. The models are applied to examples using simulated claim lag data. From Actuarial Research Clearing House, 2003, Vol. 1.

Description: This paper presents a procedure for calculating aggregate stop-loss premiums that takes parameter uncertainty into account. From Actuarial Research Clearing House ARCH 1988, Vol. 3

Description: This paper presents a method for analyzing the experience of a stop-loss underwriter to develop a claim continuance table. This method is designed to deal with the limitations presented from certain limited information so as to get the most out of the available information. To the extent this approach is succesful, the credibility of the experience analysis will be maximized, thus minimizing the reliance on external data. From Actuarial Research Clearing House 1993, VOL. 3.