Description: Life and annuity markets around the world have seen increasingly complex investment-combined products, including universal life, fixed indexed annuities, variable annuities with a wide variety of guaranteed benefits and indexed variable annuities. Insurance companies are facing unprecedented exposure to financial risks in addition to traditional mortality and longevity risks. The risk assessment and modeling for such complex designs by conventional methods often focus heavily on their market competitiveness and profitability. However, there seems to be a lack of quantifiable and systematic approaches to assess investment risks associated with investment combined products. The objective of the proposed work is to develop a framework for quantifying and analyzing various forms of contractual designs and showcase different techniques to assess the trade-off between competitive contractual designs and their practical implications regarding the difficulty of risk management.

Description: This article is the final installment of our three-part series on economic scenario generators (“ESGs”). Part I and II were published in the November 2019 and August 2020 issues of The Modeling Platform. Part III contextualizes the framework for selecting, building, and validating ESGs and focuses on the Academy Interest Rate Generator (“AIRG”), the most commonly used real-world ESG for US actuaries

Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

Description: Overview of the session “Analytics You Should Know, but Don’t” which took place at the 2007 SOA Annual Meeting in which R, an open source language for statistical computing, was discussed.

Description: Latest development in IFRS 17 including a first look at companies disclosures and financial impacts, as well as a discussion of some of the run time issues that companies face.

Description: In the United States, there has been much industry discussion of the challenges associated with a sustained low interest rate environment, which impacts both the asset and liability sides of the balance sheet in unfavorable ways. The intent of this article is not to focus on these strategic issues. Rather, we attempt to shine a spotlight on some of the modeling challenges that practicing actuaries may experience when incorporating negative interest rates into their models.

Description: As of September 2021, there are 8 recorded webinars available for free to Modeling Section members. Each of the 8 are described, as well as how to access them. There are other Modeling Section-related materials also pointed out (podcasts & newsletter)

Description: This article looks at an extensive integrated collection of financial mathematical modeling packages available in R, a language and environment 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.