Description: This paper proposes weighted fund style analysis as a better method to hedge market risks of variable annuities.

Description: Basic feature engineering uses input data to create dummy variables, log transforms, and other single-value transformations. This talk builds upon these univariate transformations and presents several strategies for creating complex multivariate features that can enhance your model's lift. Topics such as target encoding, dimension reduction/projection, row/column, and other feature engineering topics will be covered.

Description: The purpose of this article is to provide a refresher on regression and times series models so that actuaries will feel more comfortable building forecasting models.

Description: This session will explain how, by applying his or her educational background and business experience, an actuary can quickly learn the basics of data science and perform the important modeling work of insurance.

Description: A general introduction about predictive modeling in insurance applications. Some basic models are discussed, such as Generalized Linear Model(GLM), Calssification and Regression Tree Model. Other models are also briefly introduced, including data clustering, neural networks and support vector machine.

Description: This session will explore the practical aspects of bringing models to production, maintenance and management through its entire lifespan. Topics covered will include: production considerations, versioning, documentation, peer review and monitoring the model over its lifespan.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

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: This year's session will focus on the recruiting side - trends in what companies look for, trends in candidate interests, and methods for finding the right candidate

Description: This report explores conversion rates and post-conversion experience using predictive analytics.