Description: If anyone is a paradigm of the notion that learning never stops, it’s Head of Actuarial Modeling and Experience Study for Munich RE Winnie Liu. Winnie set her sights on new horizons after almost 20 years in the insurance industry. Her interest in cutting-edge predictive analytics had been budding for years, and in 2017, she set out to earn her Predictive Analytics (PA) Certificate from the Society of Actuaries (SOA).

Description: View 2018 Kaggle Involvement Program Results.

Description: Career changes typically follow a major milestone—a promotion, a job offer, a new degree, maybe a cross-country move. For Patrick Getzen, exposure to some new perspectives ignited a shift.

Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: This article provides an overview of the current Artificial Intelligence ("AI")/Machine Learning ("ML") regulatory landscape and governance considerations for insurers deploying AI/ML for their long-term care business.

Description: Article describing content and key takeaways from experience completing the SOA PA Certificate

Description: Actuaries are leveraging predictive modeling and machine learning more and more as they strive to provide greater value to their stakeholders. Actuarial standards of practice provide guidance around modeling and data. The world has changed significantly over the last few years, so it is important these models be tested for fairness as well as any statistical biases. This article addresses some of the key issues actuaries need to be aware of as Corporations and society work through the responsible us of this technology and underlying data.

Description: Technology has streamlined countless processes in our daily lives, and insurance is no stranger to this transformation. Technology can make insurance simpler and more personal for consumers, and can enable actuaries to better analyze unstructured data, make good decisions, create new products, and find channels to enhance profitability, provided they can tame the flood of information pouring in from across the digital ecosystem.

Description: SOA predictive analytics certificate program provides strong theoretical background and practical, hands-on experience that can be applied to decision making

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.