Description: This paper investigates the use of models of dependent mortality for determining annuity values. We discuss a broad class of parametric models using a bivariate survivorship function called a copula. Using data from a large insurance company to illustrate our methods, we calculate maximum likelihood estimates to calibrate the rnodel.

Description: This abstract describes a paper that introduces Technology enhanced learning tools that are available and how they will be used in classrooms and beyond

Description: Data files for presentation.

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: This study summarizes the results of the the Society of Actuaries' Non-Mortality Decrement Task Force data collection effort of plan turnover data for years 1994-2000. Service, by itself, seems not to be a good predictor of retirement. Service and age are each good predictors for termination it is found. The report is designed to offer actuaries information about turnover as it pertains to defined benefit plans.

Description: Employee termination and retirement rates affect the valuation of employee benefit plans and thus are of concern to actuaries. To provide timely experience for the profession, the Society of Actuaries Pension Plan Turnover and Retirement Rates Committee organized a data collection effort. Thirty-two contributors provided almost 1.8 million life years of pension plan turnover data for years 1994-2000. This report documents the data assembly and verification procedures used in collecting the data.

Description: This abstract describes a paper that introduces KDBINTMTM (Knowing, Doing, Being, Inspiring, and Networking), the framework in how it is being defined, and a plan to measure success for actuarial programs.

Description: This is the first article in the new "Fireside Chat" series which intends to honor highly accomplised academic actuaries and allow them to share their insights into academic careers with young scholars. This article is mainly based on an interview with Dr. Edward Frees (Jed), one of the most accomplished actuarial professors.

Description: An article in the Expanding Horizons newsletter, Issue 26, published in April 2003 that discusses the Annual Prize to the author or authors writing the best paper to appear in the North American Actuarial Journal, the leading scientific publication of the Society. It announces the winners of the award, Mary R. Hardy for A Regime–Switching Model of Long-Term Stock Returns,and Phelim P. Boyle, Adam W. Kolkiewicz and Ken Seng Tan, for Valuation of the Reset Options Embedded in Some Equity–Linked Insurance Products.

Description: The purpose of this paper is to understand characteristics of a household that drive life insurance demand with more sophisticated analytical techniques. The analysis is based on the Survey of Consumer Finances data, a probability sample of the U.S. population. Household demand of two types of insurance, term life insurance and whole life insurance, is examined jointly. We model both the frequency and the severity of demand for insurance building on the work of Lin and Grace 2007 by using covariates that they developed.