Description: This article is a continuance of a series on Canadian federal government statistical agencies. From The Actuary Vol. 24, No. 4

Description: This paper is written totally under present Indian conditions, but keeping an eye on the future, when there will be number of insurers, and this model can be applied to any condition where a number of insurers already exist. The purpose of this paper is to attempt to measure, from the point of view of a potential life insurance policyholder, the quality of the insurers and the variability between them so that it will be helpful for potential policyholders to choose their insurers according to their requirements.

Description: Economic Scenario Generator - Investment Section Web Series Session #2 - Real World This session will cover the following points: 1. NAIC ESG 2. Strategic Asset Allocation and Optimization Techniques 3. Covid-19 Postmortem: Modeling and calibration changes because of recent events

Description: This paper is concerned with the determination of credibility factors applicable to the estimation of the true claim level of a particular risk, based on both the actual observed claim experience of that risk and the expected claim level that would be predicted for the risk according to its exposure characteristics. From Transactions of Society of Actuaries 1969, Vol. 21, Pt. 1, No. 59 AB.

Description: A disscussion of actuar R, an R package for actuarial science, and its facilities in the fields of loss distribution modeling, risk theory and credibility theory.

Description: This paper investigates how the concepts of asset-liability matching can be used to shape investment strategy. From Transactions of Society of Actuaries 1980, Vol. 32.

Description: The purpose of this paper is to describe the application of the Monte Carlo technique to a practical situation in a company, group term rate making. It included a discussion of some of the problems encountered and the solution to these problems. Followed by several discussion papers submitted

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.

Description: Chapter 1 presents the background to the current study, followed by the methodology used, and a summary of the findings of the study. From Group Medical Insurance Large Claims Database Collection and Analysis, M-HB97-1.

Description: A Summary of the UVS Project by Dave Sandberg from The Financial Reporter, March 2001, Issue No. 45.