Description: The audience will learn about practical applications for actuarial and operational problems by using data science, how to collaborate effectively between actuarial, data science, data engineering, data management, legal and others and specific questions they need to ask the data science team to make effective data science solutions.

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: 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

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: Insurers have long relied on scenario analysis to understand the long-term impacts of potential business decisions, but the advent of rapidly changing market environments and a growing desire to optimize capital has spurred business leaders to leverage new tools in order to explore potential options.

Description: IUL continues to be the strongest selling UL product. Companies have great profit/pricing expectations, but are those results being achieved? Learn about potential areas of underperformance for IUL including behavior related issues, hedging inefficiency, and mortality issues. The session will also discuss ways to monitor these risks and discuss different means to address potential issues of underperformance in hedging, pricing and in force earnings.

Description: The expanded use of predictive models, algorithms, and external consumer data has drawn the attention of the life insurance industry to the matter of fairness and bias in underwriting, including proposed regulation in states like Colorado and New York and a draft NAIC Model Bulletin. Insurers will benefit taking a proactive approach to verifying that their practices, both current and future, do not result in unfair discrimination. This session will help attendees in understanding potential requirements and reporting insurers must address, techniques and tools for evaluating bias, and challenges in doing so. At the conclusion of the session, attendees will be able to: - Understand proposed regulation and practices regarding fairness and bias - Identify tools and techniques for evaluating bias in their processes - Discuss the challenges the industry will face in addressing fairness in underwriting

Description: In this session, we will provide examples and highlight real-world analytics that have been done to compare providers of tertiary and quaternary (T/Q) services (i.e., complex and potentially rare conditions). We also will address the need for analytics like this, who the potential audiences are, the data necessary to complete an analysis, and we will walk through actual case studies.

Description: Session 3A: State-Specific Enrollment Modeling - Understanding Disenrollment Trends and Population Dynamics