Description: Risk Adjustment continues to be a hot topic in Health Insurance since its emergence. This mechanism came into being as a means of reducing the risk of selection, particularly in marketplaces where underwriting is not allowed, and rating factors are significantly restricted. Risk adjustment serves to adjust premiums to reflect the risk of individual members or overall members as a whole within a particular cohort of a health plan. Risk adjustment is not perfect, as it does not perfectly predict future costs or perfectly adjust for the differences in expected costs. Different marketplaces have different challenges with risk adjustment.

Description: An article about how to determine the best time to retire

Description: The Mortality Improvement Model, MIM-2021- V3. Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the MIM Advisory Group (“Advisory Group”) developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement.

Description: The SOA Research Institute and LIMRA have completed the most up-to-date study of variable annuity contract owner behavior. Learn more about this vital resource for actuaries working with annuities.

Description: The article introduces a cashflow matching strategic asset allocation (SAA) framework for life insurers. Given the recent shift to higher interest rates after two decades of persistent low interest rates, insurers have an opportunity to enhance their asset liability management by rebalancing asset portfolios to narrow duration gaps and attain higher risk-adjusted yields. The framework aims to align investment objectives with economic, regulatory, and rating agency requirements. It prioritizes cashflow matching as the primary optimization goal and incorporates security-level asset information to assist portfolio managers in executing SAA seamlessly. The article highlights the benefits of the cashflow matching SAA framework, including greater attention to cashflow matching, unbiased results, readily executable strategies, and stable economic reserves. A case study is included to demonstrate the effectiveness of the framework in improving risk-adjusted return and cashflow matching for a representative liability block.

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: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

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: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

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.