Description: This article discusses facts and issues related to the selection of a stock’s price distribution.

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: 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 session will explore governance considerations for insurance carriers leveraging machine learning models. During the session, panelists will share insights on the evolving predictive model and AI regulatory landscape, as well as techniques for developing or enhancing AI governance frameworks that comply with emerging regulatory guidance.

Description: Both LDTI and IFRS 17 have introduced numerous new metrics with most companies having only crossed the finish line, just complying with what's needed for reporting due the complexity and large amounts of data required to simply comply. How do you leverage all the work you did to get proper business insights across the organization that tie back to these reporting metrics?

Description: Emerging Applications of ESG Methods offers ground-up insights on cutting edge applications of economic scenario generation. The course will focus on the incorporation of climate change risk in scenario modeling and investment applications of ESGs. The following topics will be covered: Approaches to climate risk modeling. Climate risk is very different than classical financial economic risk. Radical uncertainty is amenable to scenario analysis and lack of data. Building special deterministic scenarios that go beyond NGFS requirements as well as bespoke scenarios. Holistic Approach to Climate Risk Modeling. Overview of different Climate Risk Modeling Applications. Use of ESG scenarios for Strategic Asset Allocation (SAA). Liquidity risk management.

Description: As the portion of inforce business valued under PBR grows, it becomes increasingly important to not only calculate reserves for point-in-time valuation but to also project reserves. This is especially critical for actuarial functions such as pricing, business strategy/forecasting, and asset adequacy testing. In this session, we will review PBR projection techniques based on work completed by the American Academy of Actuaries when developing the PBA Projection Practice Note. In addition, this session will examine specific challenges that can arise when actuaries use models to project reserves at future dates. Topics covered include mortality improvement, credibility, scenario generation, starting asset projections, and various other technical issues related to nested modeling requiring both inner and outer loop projections.

Description: Actuaries can leverage cluster modeling to reduce model runtime and cloud compute costs, which can be a real concern as companies transition from an on-premise grid to a scalable cloud solution. We consider practical considerations associated with clustering, through both US statutory and US GAAP lenses. Lastly, the results from a variable annuity liability clustering case study are presented.

Description: At the end of this session attendees will understand how their company's existing cash flow models can be leveraged to meet the new requirements for principle-based reserves under VM-22.

Description: Driver-based forecasting is a powerful tool to predict and estimate future outcomes. In recent years, driver-based forecasting has gained significant attention as a technique for actuaries to forecast the financial performance of insurance companies. Often there can be frustration around the ability to use forecasts to manage the business. However the objective of a driver-based forecast is to break down historic and projected movement based on the key drivers which impact the results. The key is to understand what the key drivers are on forecasts and what is the relative level of impact and influence we have on the drivers. Listen in on a discussion on the core principles of driver-based forecasting, including how to identify key drivers, collect accurate data, and leverage the latest technology to generate forecasts that traditional models can't match. Explore with us the various driver-based models and how actuaries can leverage them to enhance their decision-making capabilities. Then join the conversation about the challenges actuaries face when implementing this technique and how to mitigate these problems.