Description: This article describes the financial modelers’ manifesto. Focus is on how difficult it is to model financial instruments when there is a human element involved.

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.

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: 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: Regime switching models have become a popular tool in econometric time series modeling since their introduction in Hamilton [1989].These models have been widely applied for equity-returns for investment guarantees. A prominent risk management challenge is hedging these guarantees, given the equity regime switching process. This presentation develops some methodologies for regime-switching portfolio replication and use real world data to demonstrate and evaluate their effects. A Bayesian approach for regime switching model fitting is used, which allows for the examination of the robustness of each of the optimization methods when parameter uncertainty is present.

Description: Modeling Approaches and Challenges focuses on approaches to real world simulation and the challenges and considerations in real world simulation. Topics include: A frequency domain approach to build realistic multi-horizon scenarios. (Ortec approach) Cascade models structure linking models for major asset classes and economic variables. (Moody’s approach) Overview of models, calibrations & approaches within the context of different use cases. Deterministic & stochastic approaches. Importance of model re-calibration flexibility. We will also discuss specific insights into considerations and challenges in real world simulation, which include topics like: Setting capital market assumptions and/or calibration targets. Assumptions about correlation and causality. Calibrating the model to capital markets assumptions. Validating the model against capital market assumptions. Role of history, market data and stylized facts. Importance of back testing. Calibrating the model to multiple investment horizons. Inclusion of alterative asset classes such as private equity, infrastructure, and direct real estate. Associated issues of limited data, illiquidity and greater uncertainty about calibration targets.

Description: This is the disclaimer for the Economic Scenario Generators tool available from the SOA / AAA on the SOA Website.

Description: There is widespread acknowledgement in the insurance industry that cyber risk remains an untamed and seemingly ever-growing beast. It is also unique in the family of risk exposures faced by insurers in that it is both an emerging risk and an emerged risk. What are companies doing to keep on top of this risk? What are actuaries doing to contribute to cyber risk management, and what can we learn from other industries and disciplines?