Description: The National Association of Insurance Commissioners (NAIC) is in the middle of of a project to change the real-world economic scenario generator (RW ESG) prescribed for principle-based reserve and capital calculations. The presenters will provide a brief summary of the NAIC's RW ESG project and provide a general overview of RW ESGs. In addition to that, they will explain how RW ESGs impact principle-based reserve and capital levels and consider potential differences between the currently prescribed RW ESG and the proposed RW ESG.

Description: The COVID-19 pandemic has had an impact on all aspects of daily life and its impact extends to how disability incidence may play out in the future. Product profitability will be shaped by how morbidity patterns, interest rate levels and other key drivers will play out into the future. In this session, our panel will discuss COVID’s impact on IDI morbidity experience as observed over the past 18 months, offer comparisons and insights in light of other insurance lines of business, and share emerging information on the on-going effects of Long COVID. Highlights will also include how regional differences and implications for insurers as the industry moves further into the COVID vaccine era.

Description: ALM and hedging can reap significant benefits from innovation in computation and operations research. Explore applications of static and dynamic optimization in two different ALM and hedging use cases. Insurers are increasingly moving offshore by establishing Bermuda captive reinsurers. In calculating the Bermuda Best Estimate Liability ('BEL'), reinsurers will need to segment assets to support liabilities in each treaty. A Linear Programming ('LP') problem can be formulated to tactically select assets in a way that minimizes the BEL and consequently the total asset requirement. Learn more about what US Variable Annuity Insurers face with the regulatory requirement to reflect 'future hedging strategy' in their statutory funding calculation. The traditional numerical method of finding the optimal hedging target and then allocating assets may encounter the curse of dimensionality with expanding product and economic environment complexity. A Reinforcement Learning algorithm may well fit this problem and provide insurers a solution for dynamic hedging.

Description: Asset liability management is an important task for any insurer, where tools like Excel play an important role from an analytics perspective. Dashboard tools like Power Bi and Tableau can supercharge an organization’s ability to perform ALM with real-time precision and monitoring of risk/imbalances. The dashboard can provide insights into potential risks posed by asset allocation and concentration, allowing for informed decision-making to mitigate those risks. The presenters will walk the audience through an example of ALM use case, including how to build key performance indicators such as cash flow, asset allocation, and risk exposure. Additionally, we will demonstrate how these types of dashboards can be customized to fit the specific needs of your organization.

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.