Description: This document discusses the process of obtaining regulatory approval for actuarial innovations in the United States. It includes information on the research project conducted by the Society of Actuaries and Milliman, insights from interviews with actuaries and regulators, as well as advice for presenting and defending innovative ideas.

Description: The Society of Actuaries (SOA) Research Institute released an updated report summarizing current issues regarding actuarial technology. In March 2022, for a second time, the SOA Research Institute gathered a panel of insurance industry experts for an update on current actuarial technology topics to help identify directions for future research efforts. This report summarizes their discussions on the Actuarial operating model, technology risk and open-source software.

Description: This project pertained to the development of anew Actuarial Science Learning Community model, involving four types of students' engagement: academic coursework, residential life, research, and professional development. The model provided students an immersive exposure to Data Science from the Actuarial perspective, which will result in Actuarial students greatly expanding their skill set and future job opportunities, at the intersection of Actuarial Science and Data Science.

Description: This report covers credit risk loss experience during the period 2003 through 2015 on non-Rule 144A private placement securities (and, with limited exceptions, some specific types of Rule 144A bonds) held by participating companies of the life insurance industry.

Description: As part of its ongoing effort to provide useful information on COVID-19, the Society of Actuaries issued a brief survey to its Investment Section members to gain insight as to how COVID-19 is impacting investment professionals. The survey results are intended to help members compare their thinking and future expectations of COVID-19 with their peers.

Description: This is a detailed report analyzing the 2019 Individual Life Insurance Mortality Experience, focusing on data validation, trends in mortality experience, and comparisons between different datasets. It includes insights on mortality patterns by age, sex, policy characteristics, and provides detailed methodology and findings.

Description: This report updates the Canadian Mortality Data through 2021 in the Human Mortality Database and Canadian Human Mortality Database, including methodology, data sources, and internal consistency checks.

Description: In this report, we will try to provide an in-depth review of current Machine Learning and Deep Learning models and will explain how those models works and their possible applications in the actuarial field. The recent studies and successes from the industry will also be discussed. We intend to bring more actuaries onboard about those technologies and start to think about how to bring them into our daily work.