Description: As more term insurance policies move to the post-level period, actuaries are evaluating policyholder behavior and expected mortality. Enough experience has now emerged to provide a spectrum of outcomes that can model the relationship between premium increases, persistency and mortality. The presenters will share findings and consider how different post-level premium structures produce different results.

Description: The management of the investments of an insurance enterprise has undergone a considerable evolution since the pioneering work of Redington in the mid 20th century. Best practices have evolved to incorporate ever more complex insurance liabilities, expanding investment asset classes and global market opportunities. Strategic asset allocation (SAA), liability driven investing, and other robust optimization approaches form the core of the professional management of insurance company investments.

Description: With many complex regulatory changes on the horizon as well as pressures to ensure model accuracy from auditors, regulatory requirements and new actuarial standards of practice, the importance of robust model validation is coming to the forefront of actuaries’ minds. What was previously a day-two task for many companies is becoming just as important as model development! In this session, you will hear from those that have been deeply involved in building model validation frameworks and tools in responses to regulatory changes GAAP Long Duration Targeted Improvements (“LDTI”) and Principles Based Reserves (“PBR”).

Description: Claims are insurers deepest and richest transactional data – yet we rarely unleash the full potential of this data. In this session, we will discuss potential sources and attributes of claims data, traditional approaches alongside new analytic techniques and how to integrate models into existing claims management processes. During this session, the presenters will discuss different areas of how machine learning can drive significant value unleashing the potential in claims data, these include data capture, claims management and workflow design as well as behaviour prediction. In addition, the audiences will also have opportunity to learn different use cases of how claims models taking advantage of machine learning, such as foreign death fraud detection and critical illness triage.

Description: Future mortality improvement is an important assumption in the valuation of life insurance and annuities. In this session, presenters will discuss the work of the Mortality Improvement subgroup of the American Academy of Actuaries’ Life Experience Committee and the Society of Actuaries’ Preferred Mortality Oversight Group Valuation Basic Table Team in developing mortality improvement scales for AG-38 and VM-20. In addition, two recent SOA efforts on mortality improvement will be examined. Research on developing a broader framework for modeling mortality improvement assumptions is being developed to enable U.S. actuaries additional ways to follow a consistent approach across a variety of actuarial practice applications. Results from a study on U.S. population mortality by socioeconomic category and how it fits into the broader consistent framework will be presented. Additionally, results of a survey on mortality improvement practices of life insurance companies and reinsurers in Canada and the U.S. will be shared.

Description: This 2008 supplement addresses the issues that have arisen in life insurance tax laws related to IRC 7702 and 7702A since the book was published in 2004. It also enhances and clarifies issues that were addressed in the text, adding detail and further discussion.

Description: Regulators observed a wide range of practice from early adopters of PBR in regard to the treatment of non-guaranteed reinsurance and adopted a formulaic solution on an interim basis for the 2020 Valuation Manual. A field test, supplemented with additional analysis performed using a model-office approach with representative models, is being performed over the first half of 2020 to inform regulators as they select a longer-term solution. This session will provide an update on this important issue as well as perspectives on how the resolution of this issue was approached and lessons learned for the future from those intimately involved in this work.

Description: The presenters will share information about: -Insights into causes of death through the use of predictive models, based on a reinsured U.S. life portfolio, using logistic regression models to understand three main causes of death: cancer, heart and external causes; -Opioid abuse is a widespread phenomenon with drug-related mortality rates rising steeply in the U.S. over the past two decades. But what does the most recent data tell U.S. about the outlook for Opioid-related mortality?; -'Deaths of despair' have been publicized as a cause for the poor performance of U.S. mortality in recent years. The presenters will be looking at the different causes under this label, their evolution and explore the drivers behind them.

Description: in the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. The presenters in this session present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process.

Description: Institutional investors such as pension plans and insurance companies have enjoyed strong equity returns for a length of time with few historical precedents. Nearly a decade after the end of the financial crisis, strategies to provide downside protection remain expensive and the performance of traditional risk-managed funds has disappointed investors.