Description: Pandemics are a main driver of losses for both life and non-life (re-)insurance. Until COVID-19, risk assessment mostly relied on a few historic examples, most importantly the Spanish flu. The COVID-19 pandemic has shown that other pathogens (namely the coronaviruses) need to be taken into account. Also, modern societies and governments drastically respond to novel threats and implement measures such as social distancing, mask mandates, and lockdowns. Besides the properties of the pathogen itself, these reactions and the medical progression (namely the development of vaccines) are key influences on the course of a pandemic, and thus also the resulting outcomes. To account for the new insights, join us as we take a look at a stochastic, age-structured compartmental model that is calibrated on recent pandemics with several parameters sets to model different levels of reaction speed and stringency between groups of countries. Other important factors also must be defined in close collaboration with experts. Get ready to explore the development of a model and framework that can provide potential outcomes to be used as the basis to derive both summary and tail statistics to assess pandemic risk and help price complex deals.

Description: The instructors in this boot camp session will highlight the SOA's work on an updated Medicaid underwriting margin model. This research project includes an excel model, a set of instructions and a paper describing the key components of underwriting margin, including cost of capital and risk margin.

Description: LTC insurers are beginning to implement population health programs to improve the lives of their policyholders before and during a claim. Measuring the financial impact of these programs can be difficult for a variety of reasons and multiple methodologies are used to evaluate treatments or programs. This article proposes two different methods of randomized control trials to evaluate the impact of these programs through a case study approach, along with additional potential considerations when evaluating effectiveness.

Description: The session begins with a brief introduction to Gen AI, including “what it is” and how to optimize outputs. Then, we will to provide some insight into what we are seeing within the Actuarial space – this would include the extent to which actuaries are using Gen AI on a day-to-day basis, how they are going about using it, and some predictions as to where this will go.

Description: This article explores some key challenges facing the modeling actuary related to LDTI implementation and shares associated compliance and modernization considerations.

Description: The article and guidebook of the same name use a stochastic simulation model to examine potential impacts of public pension plan risk-sharing policies on government employers and plan members. Policies examined include simple policies such as COLAs and employee contributions that are contingent upon investment returns or funded ratios, and more-complex policies similar to those used in the Wisconsin Retirement System.

Description: This article discusses how to get data into and out of R, an open source language for statistical computing.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.