Description: In the United States, there has been much industry discussion of the challenges associated with a sustained low interest rate environment, which impacts both the asset and liability sides of the balance sheet in unfavorable ways. The intent of this article is not to focus on these strategic issues. Rather, we attempt to shine a spotlight on some of the modeling challenges that practicing actuaries may experience when incorporating negative interest rates into their models.

Description: Latest development in IFRS 17 including a first look at companies disclosures and financial impacts, as well as a discussion of some of the run time issues that companies face.

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: Examine initial insights on LDTI modeling best practices, as well as high-level principles that enable successful LDTI model implementation, during this GAAP webcast. Many companies are finalizing their LDTI reporting before their "go live" date of January 1, 2023, and this LDTI Modeling Lessons Learned from AXIS, Prophet and Polysystems webcast details what they've discovered during implementations. Learn about key principles for LDTI modeling, in addition to useful functionalities from AXIS, Prophet and Polysystems software. Explore the AXIS US GAAP Historical Database for ways to import, store and manage historical data for LDTI calculations, Polysystems suite of data tools and software to support LDTI and the Prophet Disclosure Library and LDTI Toolkit that allows self-contained calculations of disclosure entries for LDTI balances.

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: Edited Content: Discover more about key Economic Scenario Generation (ESG) topics that are relevant for actuarial applications. Expect a focus on risk-neutral / market-consistent ESGs, like setting long term assumptions and scenario reduction. Long tern assumptions will focus on a basic introduction to challenges in deriving long term assumptions and key concepts to better understand how those are set. Come explore a basic overview of scenario reduction techniques and challenges as well as common sense criteria to address the reasonableness of the reduction. Provided Content: The overall objective would be to introduce key ESG (Economic Scenario Generation) topics to actuaries, with two topics below that are relevant for actuarial applications. We intend to focus on risk-neutral / market-consistent ESGs. 1. Setting long term assumptions o A basic introduction to challenges in deriving long term assumptions and key concepts to better understand how those are set 2. Scenario reduction o A basic overview of scenario reduction techniques and challenges as well as common sense criteria to address the reasonableness of the reduction

Description: Discover more about key Economic Scenario Generation (ESG) topics that are relevant for actuarial applications. Expect a focus on risk-neutral / market-consistent ESGs, like setting long term assumptions and scenario reduction. Long tern assumptions will focus on a basic introduction to challenges in deriving long term assumptions and key concepts to better understand how those are set. Come explore a basic overview of scenario reduction techniques and challenges as well as common sense criteria to address the reasonableness of the reduction.

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: Economic Scenario Generator - Investment Section Web Series Session #2 - Real World This session will cover the following points: 1. NAIC ESG 2. Strategic Asset Allocation and Optimization Techniques 3. Covid-19 Postmortem: Modeling and calibration changes because of recent events

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.