Description: Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur.

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: As the Valuation Manual continues to evolve and PBR standards for additional products are drafted and incorporated, it will become increasingly important for companies to understand where the requirements are consistent across products, as well as how they differ. This session will scan the life (VM-20) and annuity (VM-21, VM-22) PBR standards across the Valuation Manual to compare and contrast key requirements. Attendees will understand where common methods and techniques can be used across products and platforms. Attendees will also be better positioned to consider assumption and margin setting, asset and derivative modeling, and model efficiency techniques for all types of principles-based valuations. Additionally, this session will profile specific challenges that can arise when actuaries use models to project PBR reserves at future dates for internal planning, pricing, and projections across life and annuity businesses.

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: An overview of guaranteed lifetime withdrawal benefits (GLWB) on registered index-linkedannuity (RILA) products and a stochastic analysis of market risks.

Description: Article describing content and key takeaways from experience completing the SOA PA Certificate

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

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: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.