Description: In this paper an algorithm is derived for computing the distribution of aggregate claims for the individual risk theory model. From Transactions of Society of Actuaries 1983, Vol. 35.

Description: In an increasing population the number of persons falls off from age to age more rapidly than does the stationary population of the life table. This can introduce a systematic bias into life table calculations. The first part of the paper describes an iterative procedure to correct for the bias, extends the method to multiple decrement and associated single decrement life tables, and demonstrates the magnitude of the bias involved. The second part of the paper summarizes the results of applying iterative life table techniques to 165 populations which encompass over a century's experience and include countries from all parts of the world. Followed by discussions submitted by several people

Description: In this paper, a mathematical model is constructed to study the deviations of claims, investment performance, operating expenses, and lapse expenses as random stochastic processes. Discussions of the paper submitted by 2 readers follows

Description: In this paper a method is described for pricing - and reserving for- return of premium benefits under disability income policies. Followed by a discussion paper

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: Gross premium calculation methods may be arbitrarily separated into: a- Type 1 methods those which initially have commissions and other per cent of premium expenses in the denominator and b- Type 2 methods those which initially have commissions and other per cent of premium expenses in the numerator. The traditional Type 1 methods directly calculate the gross premium. The purpose of this paper is: 1. to present a Type 1 method of gross premium calculation which does the following: a Incorporates the cost of reinsurance into the gross premium calculation and may be shown as a level annual cost over the gross premium calculation period. b Calculates the effect on profit margins of a change in the retention limit without redoing the original calculation. c Calculates the premiums needed to break even e.g., amortize the initial investment in surplus at the end of each policy year as a by-product. d Calculates the profit or loss generated in each policy year or over any number of policy years for any gross premium without redoing the original calculation or first using a trial gross premium. e Directly solves for the gross premium that will produce any specified profit over any number of policy years without redoing the original calculation or first using a trial gross premium. f Directly solves for the premium that will produce a specified yield on surplus invested in new business by a flexible technique not first involving the use of the renewal net premium or any other preliminary gross premium. 2. to provide a mathematical definition of a financially meaningful average policy size which recognizes the cost of reinsurance. 3. to provide for mathematical definitions of common measures of profit, and the mathematical relationships between them. 4. To establish formulas expressing the effect of changes in various assumptions used in the gross premium calculation as a lev

Description: This article focuses on one fundamental modeling assumption — the choice of a benchmark rate or risk-free rate. The author looks at this bond market assumption as a case study of how one needs to monitor fundamental market changes for their impact on assumptions.

Description: The purpose of this article is to revise the fuzzy regression portions of earlier studies and to discuss issues related to the Tanaka approach, including a consideration of fuzzy least-squares regression models. From Actuarial Research Clearing House, 2006.

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: Recent articles addressed the subject of fuzzy regression [FR] and the term structure of interest rates [TSIR]. Their approach relied on possibilistic regression. Although possibilistic regression has been used in many applications, it has a number of limitations, not the least of which is its nebulous relation to the least-squares concept. As an alternative to possibilistic regression, this paper uses fuzzy least square regression to investigate the TSIR.