Description: From a session at the 2000 Valuation Actuary Symposium held in Washington DC, September 14-15, 2000 In this session, panelists discussed how they have leveraged cash-flow testing to: • Influence investment decisions • Analyze risks • Expand Cash flow testing models to meet management requirements • Provide relevant information to regulators and rating agencies Panelists discussed the enhancements required to meet these needs and provide guidance on communication of results to a nontechnical audience.

Description: The author comments on the article Exposure Formulas Based on the Assumption of Uniform Distribution of Deaths written by T.N.E. Greville.

Description: Table of Contents

Description: Several authors have used Fourier inversion to compute prices of puts and calls, some using Parseval’s theorem. The expected value of max[S K,0] also arises in excess-of-loss of stop-loss insurance. This is the abstract of a paper that shows that Fourier methods may be used to compute them. The authors take the idea of using Parseval’s theorem further: 1. formulas requiring weaker assumptions, 2. relationship with classical inversion theorems for probability distributions, 3. formulas for pay-offs which occur in insurance. Numerical examples are provided.

Description: Authored by a MIB Solutions team of Tom Rhodes, Stuart Klugman, Marianne Purushotham and Stacy Gill, the report summarizes the results of a study on the application and adoption of credibility theory within the life insurance and annuity industry. In addition to the report, numerical examples related to the application of credibility to company mortality and lapse data are provided in the four Excel files. This excel spreadsheet is a numerical example relating to the application of Credibility for mortality - Limited Fluctuation Method. Please see the SOA website research section for the full report on Credibity Theory Practices.

Description: "Hear AM Best rating analysts discuss the rating process and the best capital adequacy ratio (BCAR), a quantitative foundation for AM Best’s assessment of carriers’ financial strength. The benchmarking of BCAR scores for various company types (national MCOs, BCBS plans, smaller regional plans) will be discussed. Look at how changes in investment and underwriting environment impacted the results over the past several years. Further, AM Best’s view of the quality of capital and various factors impacting the overall assessment of the balance sheet strength will be discussed. By attending this session, you will: Gain an understanding of AM Best’s building block approach to ratings and how the BCAR results fit into the process. Learn the differences between RBC and BCAR models. Learn how various factors—e.g. liquidity, financial flexibility, financial leverage, the org structure—impact the final AM Best assessment of health companies' capital."

Description: With the mandated health plan transparency files, health plans and providers are now able to substantially enhance their provider contracting efforts. Although the files are large and cumbersome they are market disruptors and provide new insights for provider contracting efforts. Explore a demonstration on the type of information now available and how it can be used to enhance this from both a health plan and health care provider perspective.

Description: The life insurance industry has a lot going on. Come discover some key highlights of the life insurance market mixing quick hits with some in-depth conversations about the headlines and hard lines happening in the life insurance sector. Topics include underwriting, regulation, product changes and distribution to name a few. If you enjoy the PTI show for sports, you will enjoy hearing about the life insurance industry in this format and hosts will run down the headlines of the insurance industry in PTI fashion.

Description: This report provides a toolkit for actuaries and other interested parties to model and forecast mortality by cause and socioeconomic factors.

Description: Model runtime is a common challenge for actuaries in the life and annuity space. In particular, applications such as pricing and forecasting may require the use of nested-stochastic calculations, which may require extensive computing resources or use of rough approximations. There are also other areas where runtime may be a challenge, such as projecting certain reserves or layering solving algorithms within actuarial calculations. Recently, runtime challenges have been exacerbated by the development of increasingly complex insurance products and changes in reporting frameworks, such as IFRS 17, LDTI, PBR, and Solvency II. Many approaches have been explored by actuaries in the past to address model runtime challenges. To further this research, the SOA has recently published the paper Predictive Analytics and Machine Learning - Practical Applications for Actuarial Modeling. The authors of the paper will share their insights on innovative techniques using the implementation of artificial intelligence and machine learning (AIML). Come examine an introduction into AIML, review how AIML can be used to circumvent nested stochastic and other runtime challenges, explore case studies, review how costs and benefits can be measured and discuss model risk management considerations. Key learning objectives include: - Understanding the fundamental concepts of AIM - Understanding applications of AIML for actuarial modeling - Learning about the costs and benefits - Learning how AIML can be applied in practice to improve model runtime