Description: This paper shows how discrete multivariate analysis or multidimensional contingency table methods may be applied to data arising in actuarial work. A brief introduction to the theory is followed by two detailed examples of its application. Finally, the concluding remarks indicate how the procedure could be incorporated into a complete rate-making procedure. This work was performed for the U.S. Department of Housing and Urban Development.

Description: Read the April 2017 issue of CompAct published by the Technology Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Reviews different software and/or language choices for data visualization based on 6 dimensions. Number 4 out of a series of 5 articles on data visualization.

Description: A discussion on artificial intelligence.

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: The CSTEP 'Cluster Sampling for Tail Estimation of Probability' system is a desktop-based application to assist actuaries in efficient stochastic modeling through the implementation of Representative Scenario approaches. Its goal is to provide actuaries with an open-source HPC 'High Performance Computation' research platform to select a sample set of representative 'pivot' risk scenarios through user-specified or user-researched distance definitions.

Description: A discussion on supervised learning--the class of machine learning where a model is trained to produce a specific result for a given input.

Description: An analysis of health insurance cost and benefit trends using January 1, 2011 data from state employee medical plan renewals.

Description: "Visual storytelling is frequently used to effectively convey complex actuarial concepts to our clients and other audiences. When talking about data visualization, we often focus on the explanatory aspect. But data visualization is also a crucial tool in developing insights from large and complex data sets at every stage of data analytics spectrum: • descriptive (what happened?), • diagnostic (why it happened?), • predictive (what will happen?), • and prescriptive (how do I influence what will happen?). In exploratory and modeling stages of data analytics’ project, we let the data tell the story or explore our intuition and hypothesis through data pictures. We demonstrate the power of visual display in healthcare data analytics through vivid examples from the healthcare case studies. These illustrations of data visualization exploratory power range from assessing the quality of data or examining outliers, to exploring variables’ relationships and trends, to hypothesis testing, to guiding in feature selection and models’ comparison and evaluation, to model validation. Throughout these illustrations, we will discuss principles of graphical effectiveness, excellence and integrity."

Description: Predictive modeling continues to gain traction in the life insurance industry with the rapid growth of available data and maturity of open source tools. New methods and technology in predictive analytics have enabled the evaluation of extremely large data more quickly and allow us to incorporate features previously not considered. The presenters will provide background on predictive modeling tools and algorithms utilized by actuaries and data scientists for life insurance, highlighting applications from marketing to underwriting.