Description: The class involves a combination of presentations, class participation, in-class exercises and structured hands-on work with the MA BPT in small workgroups. Prior to this course, participants are expected to spend 3 to 10 hours preparing, depending on prior experience level.

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: As the portion of inforce business valued under PBR grows, it becomes increasingly important to not only calculate reserves for point-in-time valuation but to also project reserves. This is especially critical for actuarial functions such as pricing, business strategy/forecasting, and asset adequacy testing. In this session, we will review PBR projection techniques based on work completed by the American Academy of Actuaries when developing the PBA Projection Practice Note. In addition, this session will examine specific challenges that can arise when actuaries use models to project reserves at future dates. Topics covered include mortality improvement, credibility, scenario generation, starting asset projections, and various other technical issues related to nested modeling requiring both inner and outer loop projections.

Description: While our ability to access and analyze large quantities of data is better than ever, many long-term products suffer from sparse data in critical risk areas, such as deep in-the-money lapse experience and long-term utilization rates for income guarantees. The presenters will explore industry-level annuity experience data for these types of situations, and discuss quantitative approaches to addressing them in pricing and other applications.

Description: To discuss the principles to be followed in the determination of dividend distribution scheme, how to ensure the fairness and sustainability of dividend distribution, and the specific issues to be considered in the practice of dividend distribution.

Description: Due to the development of mobile internet, Chinese insurers now have a variety of valuable ways to collect and use customers' health data.The speaker will share his experience in heath data application in the following fields:Using data to customize health information and suggestion for customers;Using data to stimulate customers' needs for insurance products; and Using data to provide tailor-made products and services

Description: The prospects for China's health insurance has increased significantly in 2018, incentivized by a series of favorable government policies to support the development of commercial health insurance. However, the market still lacks relatively mature products and services. Overall there is a common understanding in the industry that good coverage plus service is the foundation for health insurance future development, and product innovation plus care management is key for an insurance company to maintain sustainable development in the competition. In this presentation, Dr. Yang will discuss the market trend both at home and abroad with respect to health management as well as its impact on product design, pricing and cost control. With the development of internet and big data, today’s health actuary has a special role in taking advantage of the digital transformation and achieving a high-quality and efficient health care service model.

Description: Most actuaries deal with model development at some point in their career. However, very few of us have proper training in programming, software engineering or software shop operations. Explore topics, techniques, and practices from the FinTech and IT industries that can be applied to building robust, high-quality actuarial models. This session will be of particular interest to actuaries engaged in 'in-house' model development, managers of modeling teams, and actuaries interested in software development.

Description: To evaluate the risk associated with personal auto coverage, insurers have traditionally relied on information about the driver (such as age, gender and marital status), location (such as territory or ZIP code), the intended use of the vehicle (such as business or pleasure) and information about the vehicle when new (such as original cost). Insurers then added detail to driver information using sophisticated credit scoring and demographic models, and added detail to vehicle use information with telematics and usage-based insurance pricing. Now, insurers are adding detail about the vehicle itself using vehicle history. The presenters will discuss how carriers use vehicle history to more accurately price and underwrite risks. They will discuss obvious data elements, such as salvage title brands, verified average annual mileage and severe damage, as well as data that may not be as obvious but has proven predictive of loss in the past. Finally, using vehicle history as an entry point for UBI programs will be covered.

Description: Discussion of machine learning applications currently being used to augment traditional actuarial modeling in the life insurance industry. This includes data science techniques for: Quantifying and explaining differences between model versions or reporting periods Building proxy models for actuarial model output using machine learning One-class classification for data integrity checks Applications of unsupervised learning (e.g., clustering and principal components analysis) to reduce model run time