Description: The article explores a closed-form forecasting method that combines Exponentially Weighted Moving Average (EWMA), weighted linear regression, and ridge regression (L2 regularization). The approach addresses two key challenges in time-series forecasting: adapting to nonstationarity and mitigating overfitting due to multicollinearity. EWMA assigns more weight to recent observations, while ridge regression penalizes large coefficients to stabilize estimates. A cross-validation strategy across rolling time windows selects optimal hyperparameters: the EWMA decay factor (α) and the ridge penalty (λ). The method is tested using repeated regressions of stock returns, showing that most hyperparameter combinations outperform ordinary least squares (OLS) under the (λ=1, α=0) setting.

Description: This report covers the latest emerging trends in U.S. population mortality. The SOA relied upon data furnished by the Centers for Disease Control and Prevention (CDC). Observations are based on the CDC’s recent release of 2021 mortality experience, along with prior mortality experience data from 1999 through 2020.

Description: This dissertation by Tia Sawhney will help you understand this issue and start measuring it, or start promoting equitable public policy.

Description: This e-course will introduce you to a set of advanced business analytic techniques. We define advanced business analytics as a set of tools and techniques to assist in key business decisions.

Description: The global doubling of information content has been estimated to occur at a rate of every 12 hours, and conceivably faster. This ‘infowhelm’ is a double-edged sword for insurers. While maximization of the global knowledge base can fuel innovation, it can occur only in the context of reasonable cost for a higher benefit. The insurance industry – underwriters and actuaries in particular – are challenged to constantly examine new innovations that promise to raise confidence and protective value for insurability, within legal limits. This report aims to be the first of several to alert the SOA of promising innovations for the profession.

Description: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.

Description: This article discusses the use of parametric distributions to model health care cost.

Description: A discussion on how the look of charts and graphs can affect comprehension of data.

Description: This practical session would provide a technical overview about Generalized Linear Model (GLM) and describe how GLM can be applied as a business tool in life insurance. The session would also focus on case-studies sharing across different functional areas such as pricing, fraud detection, underwriting, product development and other real-world applications.

Description: This practical session would provide a technical overview about GLM techniques and describe how GLM can be applied as a business tool in solving business problems in life insurance. The session would also focus on recent real-world case-studies across different functional areas like pricing, fraud detection, underwriting and product development.