Description: This article discusses a run on the bank that occurred in the Korean securities market.

Description: Experts' discussion of mortality laws and models. From 2005 Living to 100 and Beyond Monograph, M-LI05-1.

Description: Article recapping prizes for the five best presentations given by graduate students at the 43rd annual ARC.

Description: The Society of Actuaries' Committee on Life Insurance Research, Product Development Section and Reinsurance Section are pleased to make the following report available. Authored by Allen Klein and Karen Rudolph of Milliman, this paper presents the results of research that identifies laboratory tests that are not widely used in the life insurance industry but could have potential benefits for use in the life insurance underwriting process. In addition, the authors have provided an Excel workbook illustrating the calculation for determining mortality savings.

Description: Expert discussion of three previous papers: Makeham-Type Mortality Models Mumpar-Victoria, An Extreme Value Analysis of Advanced Age Mortality Data Robertson, and Living to 100 and Beyond: An Extreme Value Study Han. From 2005 Living to 100 and Beyond Monograph, M-LI05-1.

Description: This discussion provides an overview of the SOA research process, reviews the new turnover tables and efforts to model probable outcomes of proposed legislation on the U.S. economy. From Record of Society of Actuaries, Vol. 22, No. 3.

Description: Panelists at this session of the SOA 1998 Maui II Spring Meeting discuss details of the turnover assumption for the valuation of pension plans. A recently released SOA Turnover Study is discussed.

Description: As a life reinsurer, understanding mortality risk is mission critical. Superior models of mortality risk lead to more competitive pricing in profitable market segments and more efficient use of capital. Surprisingly, modern statistical tools such as predictive modeling have not previously been used to model mortality data to gain this edge. In this paper, we present the use of a predictive modeling technique called projection pursuit regression PPR to model mortality data. We first describe the process of creating a PPR model using inter-company mortality data collected by the Individual Life Experience Committee of the Society of Actuaries, and present the resulting model. We then discuss the process of using this model as a starting point for creating a more detailed PPR model of internal company mortality data.