Description: The Society of Actuaries (SOA), in a joint project with the Institute and Faculty of Actuaries (IFoA) and the Canadian Institute of Actuaries (CIA), are creating research projects and education materials on Modeling and Management of Longevity Risk.

Description: This document is a Request for Proposals (RFP) issued by the SOA Research Institute focusing on advancing catastrophe modeling and sustainability in Asia, particularly in ASEAN markets. It outlines research objectives, proposal requirements, selection process, submission details, and other pertinent information for interested researchers.

Description: This abstract describes a paper that proposes a multi-population Global Vector Autoregression (GVAR) mortality model, in which the joint mortality dynamics of all populations are formulated in a large vector autoregression model with a set of contemporaneous global mortality factors.

Description: In December 1997, the Society of Actuaries held a conference whose focus was actuarial and financial modeling. During the course of that conference, one of the speakers mentioned a number of technologies his firm was using for modeling, which were either unfamiliar or only vaguely familiar to some of the audience. The purpose of this paper is to define each of the technologies mentioned during that presentation and to briefly describe their distinguishing characteristics. The technologies discussed in this paper are statistical pattern recognition [SPR], neural networks [NNs], genetic algorithms [GAs], simulated annealing, fractal analysis, chaos theory, rule induction, data fusion algorithms [DFAs], data compression, fuzzy logic, case-based reasoning, and expert systems.

Description: The session offers high-level initial steps to get started using R, as well as a list of helpful resources for both general data science and actuarial science with R. Attendees will learn about popular R packages to perform practical tasks such as importing, transforming and visualizing data, as well as packages commonly used by actuaries. You will also be introduced to the markdown package for creating reproducible and reusable analyses and workflows. By the end of the session, you will be familiar enough with using R and RStudio to begin incorporating these tools in your day-to-day work as an actuary

Description: There is widespread acknowledgement in the insurance industry that cyber risk remains an untamed and seemingly ever-growing beast. It is also unique in the family of risk exposures faced by insurers in that it is both an emerging risk and an emerged risk. What are companies doing to keep on top of this risk? What are actuaries doing to contribute to cyber risk management, and what can we learn from other industries and disciplines?

Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.

Description: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.

Description: This presentation illustrates a calibration model against 60 years of historical data using a pragmatic mixture of filtering, maximum likelihood and method of moments techniques, and compare the resulting model with interest rates in contemporary economic scenario generators.

Description: This abstract describes a paper that investigates model uncertainty arising from different ways of treating the operational loss data collection threshold.