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: Actuaries pride themselves on innovation and advancements; but no professional field has pushed to set the bar higher than medical Radiology. Over the course of the last few decades, medicine has begun to rely more on artificial intelligence to aid in the process of patient care; and the results have been outstanding! This article looks to show how high the bar has been set, how it will impact the current state of the world of the actuary and the future impacts

Description: The article investigates the reasons behind non-zero financial implications for fully reinsured businesses within the regulatory frameworks of US GAAP Long Duration Targeted Improvements (LDTI) and International Financial Reporting Standards (IFRS) 17. It helps companies grasp the nuances of these standards, thereby enhancing their risk management strategies, improving regulatory compliance and optimizing their decision-making processes.

Description: The introduction and effective application of US GAAP LDTI have increased the scrutiny of those reviewing the updated financial results. That is because LDTI has resulted in significant changes in actuarial reserving and deferred acquisition cost (DAC) models for long-duration contracts, including significant changes to data, assumptions and required disclosures. In light of the changes, greater reliance has been put on companies’ model risk management (MRM) teams and model validations to assess the conceptual soundness and accuracy of their companies’ actuarial models and to assess that the underlying data and process utilized are suitable and appropriate. This article covers a brief background on model validation, walk through a sample LDTI-focused model validation framework/test plan, then provide insights on common LDTI-related validation observations based on the authors' prior validation experience.

Description: This article describes how insurers can expand the reach of their efforts with regard to Economic Capital EC into the broader enterprise risk management process. Some experts contend that can be done by shifting the focus towards value. EC has the potential to shed new light on valuable opportunities for insurers.

Description: This article discusses the guidance provided in IRS Notice 2022-6 and prior guidance on whether distributions from an individual account under a qualified retirement plan satisfy the exception to the 10 percent additional tax in Internal Revenue Code section 72(t) for certain substantially equal periodic payments.

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

Description: In July 2007, Mike McLaughlin, FIA, FSA, MAAA, became one of the first individuals to add a new credential from the Society of Actuaries after his name: CERA. Short for Chartered Enterprise Risk Analyst, the CERA is the first new credential from the SOA since it was formed in 1949. While the development of this new international credential was the work of many members over the past few years, McLaughlin was at the helm. From The Actuary Magazine October 2007

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: This article is aimed to cover the following: 1) Provides an assessment of income volatility under Long Duration Targeted Improvements (LDTI) for several key types of long duration contracts along with select illustrations, and compares against those under current GAAP, 2) Discuss areas where accounting policies or methodologies can influence income volatility, and 3) Show how a company's management reporting can be adapted to help explain income variances under LDTI.