Description: Interview with Milliman consultant Sarah Konrad Hinchey

Description: This article provides information on how to navigate the Internet's World Wide Web and tap into resources of interest to actuaries.

Description: The conventional way to analyze grouped observations on a single variable is to construct histograms or, sometimes, fit a normal distribution. We extend this latter approach by allowing for transformations to near normality.

Description: 2016 marks the 60th anniversary of the Canadian Reinsurance Conference. This one day industry meeting hosting delegates from North America and beyond will be held in Toronto on April 13, 2016 under the theme "ReDefining Leadership".

Description: This is the inventory part of the report from the research project that reviewed and inventoried the range of quality and efficiency measures currently available for healthcare. The goal was to outline key areas of quality and efficiency measurement and identify future opportunities for actuaries. The inventory lists the resources and provides brief descriptions of their services with links.

Description: Meet Christine Hofbeck. Christine is pushing the boundaries of traditional life and general insurance by finding problems and fixing them with new technology, modeling methods and sources of data. Most recently, she built a predictive analytics team at one of the world's largest financial services institutions to support the pricing team for a $5 billion portfolio covering life, disability, critical illness and more.

Description: Presented at October 1986 Annual Meeting. This session discussion is about profit analysis and elements of profit such as: interest, mortality, lapse experience, expenses, reinsurance, taxes. It also about use of profit analysis as a business tool to drive management action.

Description: A discussion on artificial intelligence.

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: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.