Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: Review of non public options to finance care including personal savings, revers mortgages, family care and private insurance.

Description: Pandemic impact on continuing education. Financial Reporting Section and Community Engagement. US GAAP textbook update for LDTI. IFRI textbook update for amendments.

Description: The article examines the impact of the COVID-19 pandemic on U.S. mortality trends, noting that despite a decrease in COVID-19 deaths due to better treatments and vaccinations, excess mortality persists, especially among younger adults due to accidental causes. It discusses the challenges of predicting future mortality trends, considering factors like medical advancements and societal changes. The article also highlights the potential of new medical technologies and AI to significantly improve health outcomes in the future. Overall, it underscores the complex interplay of various factors influencing mortality in the post-pandemic era.

Description: This article reviews how to earn and keep track of CE credits for the SOA CPD requirement. The requirement begins on January 1, 2009.

Description: This article provides a brief description of the changes in the financial mathematics exam beginning in 2007.

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: 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: Describes a process to develop a good forecast. Also provides an example.

Description: Under long-duration targeted improvements (LDTI), there are a few insurance liability balances that now are required to be fair valued, market risk benefit (MRB). The session will start from all the applicable balances that are required to be fair valued under LDTI ((i.e., MRB, financial accounting statement (FAS) 133, value of business acquired (VOBA) upon business combination)). The session will use a case study to discuss some of the nuances and general considerations for an MRB, FAS 133 and VOBA balances for a business combination for an fixed indexed annuity (FIA) contract. The case study will discuss some MRB fair value considerations and followed by FAS 133 fair value techniques. It will go over VOBA fair value estimation by using actuarial appraisal method with a few twists (e.g., using market yield in generating distributable earnings is better than using book yield and adjusted to bring it to fair value later on).