Description: Feature article discussing the ways artificial intelligence will influence the actuarial field.

Description: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.

Description: A discussion on using the Cox Proportional Hazards Model in developing a predictive underwriting model that produces a mortality multiplier for each individual.

Description: Feature article discussing the usefulness of Lorenz and Lift curves.

Description: A discussion about how many models can address the over fitting issues caused by too many variables by regularization, variable selection is still meaningful.

Description: This article discusses techniques used to create risk scores to help determine appropriate primary care panel sizes for a provider organization.

Description: This article takes the reader on a speculative trip to the not too distant future to explore a hypothetical new life insurance company that has completely reinvented the way it does business, and explores some of the potential benefits to consumers and the company’s stakeholders.

Description: Identifies current trends and considerations when setting the mortality improvement assumption for individual life insurance in the US.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.