Description: The IFRS 17 implementation deadline is near and companies have decisions to make. While many decisions pertain to specific IFRS 17 requirements, others are more strategic in nature. This session will explore key considerations and decisions faced by stakeholders as they finalize their implementation work and determine ongoing best practices in light of this new standard.

Description: This session will discuss two important dimensions of why a holistic approach is recommended over a piecemeal, nonintegrated approach for an IFRS 17 modeling solution. Attendees will receive three perspectives from actuaries working as actuarial consultants or within an insurance company or as a software developer. The first dimension is model design. The session will demonstrate holistic model design advantages with regards to model inputs, model outputs, liability portfolio management, modeling efficiency, and transparency. The second dimension represents organizational impacts resulting from the model design.

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: Actuarial models and modeling processes continue to grow in complexity, accelerated by the changes in U.S. Statutory, U.S. GAAP and IFRS reporting requirements in the past few years. Modeling and valuation teams are expanding their toolkits beyond the traditional vendor-maintained actuarial software and the MS Office suite of tools. Companies have started to use a variety of tools to improve model dev and valuation processes, including: - Data management - Automation and workflow - Reporting and analytics

Description: Models are a staple of an insurance company's operations. However, models that are not appropriately governed may lead to underpriced products, under-funded reserves, unfairly treated customers, and potentially catastrophic operational, legal, and reputational risks. Model Risk Management (MRM), an essential component of an insurer's ERM function, needs to keep pace with the rapidly evolving model ecosystem to limit model risk and produce results that management can trust when employing company strategy. With the adoption of ASOP #56, emerging technologies, and regulatory changes such as PBR, LDTI and IFRS 17, it is increasingly important for MRM frameworks and processes to adapt, upgrade and evolve. Discuss insights on how to embed ASOP 56 into Model Risk Management (MRM) and assess the design, development, modifications, and validation of actuarial models, the advancement of analytics and technology that empower insurers for process optimization and automation, as well as the the key components, stakeholders, best practices, and strategic benefits of a comprehensive MRM framework. Go over use cases on enhancing MRM in the context of ASOP 56, technology, and model governance. Explore how automation and optimization of the MRM process will allow companies to realize the strategic benefits of MRM, ensure successful adoption of regulation changes, and the pros and cons of emerging technology on MRM. By attending the session, you will be able to: - Describe how ASOP 56 can help improve MRM processes. - Describe the key components, stakeholders, best practices, and strategic benefits of a comprehensive MRM framework. - Identify where and how to use technology to automate and optimize the MRM process. - Evaluate the impacts of emerging technology on MRM.

Description: Best-in-class analytics allow companies to quickly understand results and dynamically manage their business. When enhancing analytics, we can learn from companies that have recently deployed new dashboards for Long Duration Targeted Improvements (LDTI), International Financial Reporting Standards 17 (IFRS17) and principle-based reserving (PBR). Data transformations and new software tools provide significant opportunities, but many companies are grappling with an actionable path forward to implementing new analytics.

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: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."

Description: "Can you answer the question 'what are the chances we will lose more than $1 million next year?' without saying 'it depends'? If you miss a projection, can you quantify the chances that the miss was due to random variation vs. an incorrect projection? ten in as presenters demonstrate how to answer these questions and others using total risk analysis. Total risk analysis includes quantifying both the random variation risk and the projection risk associated with actuarial functions like pricing and reserving. After attending this session, you will be able to describe total risk analysis, apply total risk analysis to many actuarial functions."

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."