Description: Because actuaries fit models for a variety of situations, particularly with regard to data modifications, it is useful to have a single approach. Though not optimal or exact for a particular model or data structure, the method should be reasonable for most all settings. Such a method is proposed in this article. Actuarial Research Clearing House 2003, VOL. 1

Description: The paper discusses the optimal design of dividend - or premium refund - formulas that occur in connection with group insurance contracts. Sample formulas are provided and analyzed from a statistical analysis perspective . Followed by 2 discussion papers submitted

Description: This session at the SOA 1990 Hartford Spring Meeting is about cash flow testing as it relates to taxes including the regulatory background, current practices, and the effect of using tax versus statutory values in cash flow testing.

Description: This paper explores available disability experience data in order to shed some light on the character and extent of the subjective influences which affect the frequency and duration of disability. A mathematical model for computing disability income premiums and reserves is also presented.

Description: This paper expands the calculation of a group's credibility factor from a traditional univariate one-dimensional single layer model to: 1. a univariate one dimensional two layer model, 2. a multivariate one dimensional single layer model, and 3. a univariate two dimensional single layer models.

Description: Read the August 2020 issue of The Modeling Platform published by the Modeling Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: This paper focuses on business risks rather than market, credit and operational risks. The author proposes a bottom-up approach for modelling and measuring business risk where the dynamic of the underlying earnings is described in a continuous-time model.

Description: The insurance reserve for an individual contract can be pictured as a quantity which changes continuously from moment to moment in response to the forces of interest and mortality. A different mathematical model arises for each method of defining the death benefit, and in this paper a number of such mathematical models are analyzed. Followed by discussion papers submitted by several authors

Description: This teaching session was presented as Session 6B at the 1987 Valuation Actuary Symposium. The subject was modeling techniques for use in testing the scenarios being developed by the Canadian Institute of Actuaries, ‘CIA,’ Solvency Standards Committee. The presenters, members of the Committee, have been working on developing a generic modeling approach. Their presentation is limited to the first of three phases of proposed CIA standards for solvency reporting dealing with scenario testing, namely the projection of surplus under various prescribed and selected scenarios.

Description: Automobile insurers classify the risks that they underwrite in order to subdivide the portfolio into classes of risks with similar profiles. While some insurers may have sufficient historical data, several others may not have significant volume of experience data in order to produce reliable claims predictions to help enhance their risk classification systems. A database containing a pooled experience of several insurers thereby helps to produce a more fair, reliable, and equitable premium structure for all risks concerned. This paper uses multilevel models to analyze the data on claim counts provided by the General Insurance Association of Singapore, an organization consisting of most of the general insurers in Singapore. The multilevel nature of the data is due to the following: a certain vehicle is observed over a period of years and is insured by a particular insurance company under a certain 'fleet' policy. The authors show how intercompany data lead to a priori premiums and a posteriori corrections to these initial premiums. Specific focus is made in understanding the intercompany effects using various count distribution models [Poisson, negative binomial, zero–inflated and hurdle Poisson]. The performance of these various models is compared the authors also investigate how to use the historical claims of a company, fleet and/or vehicle in order to correct for the premium initially set.