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: 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: Can the aggregate results from a group be used to forecast something as subjective and judgmental as one single person’s opinion? Forecasting of this type commonly uses a set of techniques known as collaborative filtering which is discussed at length in this article.

Description: The main focus of this article revolves around pension plans and their funding levels.

Description: Forecasting & Futurism

Description: An introduction to the Forecasting & Futurism section's 2014 contest, where participants are encouraged to explore a forecasting application and develop it. The article discusses the contest, the scoring, and the prize.

Description: July 2012 issue of Forecasting & Futurism Newsletter

Description: This actually is a copy of an appendix of a research paper sponsored by the SOA Committee on Finance Research. You can read the entire research paper: “Lapse Modeling for the Post-Level Period—a Practical Application of Predictive Modeling” at the SOA site https://www.soa.org/Research/Research-Projects/Finance-Investment/lapse-2015-modeling-post-level/#sthash.W9lERSls.dpbs, but you can read this valuable appendix copied here in this issue to see how to build a PM step-by-step using your data and the R programming language. We thank Richard Xu, Dihui Lai, Minyu Cao, Scott Rushing, and Tim Rozar for their excellent paper and the Society of Actuaries, for the permission to reprint this portion of the paper.

Description: Read the December 2017 issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section.

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.