Description: This article describes how to externalize code of Moody's Axis platform to Visual Studio, so that all the actuarial calculation code run through corporate DevOps pipeline. This article gives specific examples of the author has used in his job to accomplish automated model governance.

Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.

Description: The traditional defined-benefit (DB) plan is a type of plan structured to provide the greatest degree of retirement income security, designed to pay out a guaranteed retirement income for the life of the retiree (and, when applicable, a beneficiary). However, over the last several decades, the number of private-sector employees covered under these traditional plans has decreased dramatically and been replaced by defined contribution (DC) plans. In contrast, public-sector retirement plans, which are exempt from ERISA, have seen a much smaller decline in DB plans. There are many differences in the private and public sector that likely contributed to this phenomenon, but ERISA and the many amendments to it has been a major factor.

Description: This paper applies the research in Prospect Theory of Kahneman and Tversky to explain why sales of annuities lag sales of life insurance products. The author theorizes that life insurance is easier to sell because customers perceive themselves to be in the domain of losses, while potential annuity customers perceive themselves to be in the domain of gains. Kahneman and Tversky showed us that people are mostly risk averse in the domain of gains and risk seeking in the domain of losses. Purchase of an annuity or life product is a risky investment. Customers will find it easier to take the risk in the life insurance domain of losses than the annuity domain of gains. The author then proposes a hybrid life annuity product that might address both mortality and longevity risk in one product that would allow both products to be more competitive than stand-alone products.

Description: "How long is long in longevity? – Jesús-Adrian Álvarez, Ph.D. - Danish Labour Market Supplementary Pension Fund (ATP), The Rate of Aging in humans is constant. New evidence – Marcus Ebeling, PhD., Research Group Mathematical and Actuarial Demography, Max Planck Institute for Demographic Research, Rostock, Germany, Department of Sociology and Demography, University of Rostock, Rostock, Germany; Roland Rau, Ph.D., Professor, Research Group Mathematical and Actuarial Demography, Max Planck Institute for Demographic Research, Rostock, Germany, C6 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Håkan Malmström, Ph.D., C6 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Anders Ahlbom, Ph.D., Professor Emeritus, Karen Modig, Ph.D., Associate Professor, C6 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Human Lifetimes: Finite But Not Necessarily Bounded – Fei Huang, PhD., School of Risk & Actuarial Studies, UNSW Sydney; Ross Maller, Ph.D., Emeritus Professor, Xu Ning, Ph.D., Research School of Finance, Actuarial Studies & Statistics, Australian National University, Brandon Milholland, Ph.D., IQVIA Plymouth Meeting, PA."

Description: "Minimum Social Security Benefit (s) and the Alone Stage of Retirement in the U.S.? – Lori J. Curtis, PhD., Douglas Andrews, FSA, FCIA, Ph.D., University of Waterloo The Importance of Cognitive Health – Gordon Woo,Ph.D., RMS LifeRisks"

Description: This three-part series provides an overview of the rapidly evolving and dynamic gene and cellular therapy landscape, from the clinical, historical, and risk transfer perspectives. It covers the actuarial considerations in pricing cell and gene insurance and reinsurance, the value assessment of the therapies, manufacturer pricing, and reinsurance cost.

Description: Starting with a review of recent VA and FIA industry policyholder behavior experience, this session will explore several critical elements in developing a sustainable and coherent framework to translate this complex experience data to assumption models, particularly for guarantee features such as GLWBs. The discussion will include the use of industry- and company-level data in building customized predictive models, and integrating models across behaviors such as income utilization and surrenders.

Description: Reinsurance has been available for VA Guarantees for over 20 years. During that time, the reinsurance market for VA Guarantees has undergone frequent and sometimes violent transformations. Where does the market stand today? What forms and structures of reinsurance are available? Which VA Guarantee designs and features are easier to reinsure? Is reinsurance offered on both newly issued policies as well as in force/legacy books, and which vintages are difficult to reinsure? In an age of sophisticated hedging programs, what role does a reinsurer play in helping a VA writer manage this highly volatile risk? In this session, we will hear from a reinsurer that is actively writing new VA Guarantee reinsurance treaties in the U.S. and around the world, and from a VA writer that has successfully partnered with a reinsurer to enhance its VA product offering.

Description: With the rapid development of the Insurance market, insurance technology—including online insurance--continues to mature. Zhongan Online invests heavily in fintechand Zhongan Answer, an insurance robo-advisor, launched in 2018, could improve transaction efficiency by using artificial intelligence to communicate in a timely manner and solve the pain point of the perplexed insurance clause. Under a different consumption scenario, Zhongan Answer could reply to users in a timely manner, while also implementing private customized products and services by providing users with personal risk management and insurance assessment solutions so that users could easily and conveniently select the most suitable insurance plan. The intelligent shopping guide system of Zhongan Answer could improve transaction efficiency by analyzing the personal insurance intention and providing the humanized interpretation of insurance clause. Zhongan Answer has served millions of users and supported more than 100,000 insurance-related knowledge points with an accuracy rate over 90%. Zhongan Answer has provided more than four million Q&A services, and its intelligent shopping guide system covers more than 90% of insurance products.