Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: The paper demonstrates that the most popular rationalization for evolving glide paths is flawed and should be discarded. The paper also demonstrates that glide paths should generally evolve for investors that endeavor to optimize outcomes and reevaluate their portfolios regularly.

Description: This is a notice is a call of papers reflecting actuarial and insurance related themes.

Description: A significant fraction of insurance risk is characterized by the tail of the claims distribution, where we have much less data to model probabilities accurately. Ignoring the tails can lead to underpricing of tail risk and mispricing. Estimation of the tails can be complex due to a bias-variance tradeoff--the further out we go into the tail the more accurately we can model the extreme tail (lower bias) but the less data we have (increased variance). The statistical tools of Extreme Value Theory can be leveraged to more accurately estimate both the incidence and the severity of extreme events given that they occur. using the Generalized Pareto Distribution.

Description: Column highlighting education committee volunteers and the benefits of volunteering.

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: 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 discusses considerations for statistical biases that may arise in LTC blocks due to historical benefit reductions. The article explores these biases in the context of experience studies and experience projections.