Description: Following recent market volatility and regulatory changes, market data and market-consistent economic scenarios are at the core of the valuation of liabilities and market risk benefits under regimes such as IFRS 17 and Long Duration Targeted Improvements. This webcast will explore options available to insurance companies to establish stable long-term volatility assumptions and discuss how the choice of financial data can impact the calibration and the simulation of credit spread scenarios.

Description: You will be reminded how to spot and avoid statistical biases and fallacies that are common to health care actuarial data and problems. We plan to explain various statistical biases and fallacies and for each show one or two examples of how they might pop up in healthcare data and what strategies can be used to avoid/account for them. Likely topics include simpson's paradox, regression to the mean, base rate fallacy, sampling bias, survivor bias, Berkson's fallacy, and others.

Description: Come gain insights into the techniques used in Machine Learning and its applications. You'll discover the key technological advances in recent years which were behind and ultimately facilitated the breakthroughs including ChatGPT/GenAI. It's time to uncover real-world use cases where AI models and their transformative impact on our lives.

Description: One common barrier to employing GLM modeling is the lack of sufficient data. This limitation often comes from the combination of the considerations in ASOP No. 25 “Credibility Procedures” and the fact that GLMs assign full credibility to the data. Explore the possibilities of credibility-based regression by expanding the intended purpose of modeling analysis from producing a single model as output to producing data insights, credible differences, and a range of reasonable models.

Description: Our panelists will explore fixed income topics from an actuarial perspective. We'll start with the basics of bonds, including sensitivity measures like duration and higher moments. Then, we'll discuss bond ALM characteristics, highlighting the advantages and disadvantages of incorporating various bonds into a portfolio. Finally, we'll cover actuarial modeling considerations, such as reinvestments, defaults, expenses, and other assumptions. We encourage the audience to ask questions throughout the presentation and we hope you can join us for an engaging discussion!

Description: As companies have reported their latest financials for 2023, LDTI is in its full swing for the public companies. Under US GAAP reinsurance accounting methods can be daunting. Add onto this legacy practices related to reinsurance reporting and this may lead to significant unwinding and implementation efforts. This session will discuss industry practices commonly applied to US GAAP reinsurance reported balances such that LDTI adopters can gather deeper insights into the LDTI impact. Specifically, the session will explain the two most popular methods to value reinsurance under LDTI, methods applying a direct measurement of the reinsurance net cost and a method that recognizes ceded asset balances and a cost of reinsurance (“CoRe”). The session will present simplified direct and ceded models to demonstrate how the actuarial balances under LDTI impact GAAP book profits for different underlying products and reinsurance types.

Description: The Inflation Reduction Act represents the most significant changes to the Part D program since its inception 20 years ago. The implications will not only alter the benefits offered by Medicare Advantage (MAPD) and Prescription Drug Plans (PDP) but will alter the Part D cash flows and the seasonality of such amounts for interim reporting periods. In this session, we will provide a brief overview of the IRA changes and dive into how the changes could impact the 2008 Medicare Part D Accounting Practice Note.

Description: Artificial intelligence is rapidly transforming the investment landscape. From natural language processing and large language models to physical AI applications like robotics, autonomous vehicles, and drones, the evolution of AI is reshaping both how we analyze data and where we find opportunity. We’ll begin by exploring the evolution of AI, which is already driving disruption across industries such as coding, customer service, and marketing. However, the future of AI is physical AI (drones, AVs, robotics). With valuations already stretched, and growing concerns about the “circular economy” and bubble risks, investors should focus on high-quality tech companies that generate free cash flow and deliver strong returns on invested capital. We'll explore examples of interesting companies across the globe: Meta, Alphabet, and Uber in the US; ASML in Europe; Tokyo Electron in Japan; TSMC in Taiwan; and BYD in China. The session will also examine how natural language processing and large language models are reshaping the way quantitative and fundamental managers extract insights from raw data. We’ll highlight how these tools are being deployed to enhance understanding of risk and return, and how they’re increasingly supporting collaboration between quant and fundamental teams.

Description: Join us for this popular annual webcast covering the latest considerations for the year's U.S. statutory financial reporting work. Always informative and occasionally entertaining, this webcast offers a chance to hear from three expert speakers, who will discuss asset adequacy testing and this year's New York and California holiday letters, the impact of recent interest rate changes, potential changes to statutory financial reporting due to climate change, and recent proposal(s) impacting asset adequacy testing for reinsured business.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.