Description: Up until recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops . In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents a promising prospect for overcoming problems of big data.

Description: This article goes through some basic data exploration techniques to show how different approaches look.

Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.

Description: A description of the Technology Section's goals and accomplishments.

Description: Letter from the editor describing the contents of the issue.

Description: A review of the Bridging the Gap Series: Application of Predictive Modeling in VA/FIA Risk Management at the Equity Based Guarantees Conference in Chicago.

Description: Research project that reviewed and inventoried the range of quality and efficiency measures that demonstrate the performance of hospitals and physicians. Besides outlining key areas of consideration for quality and efficiency measurement, this report also describes future opportunities for actuaries and other health professionals interested in this evolving area. The inventory part of this report is in a separate document.

Description: The guest editorial details some valuable lessons during the author’s career as an employee benefits (mostly retirement) consultant, turned P&C predictive modeling, reporting and data warehousing lead, turned an analytic strategist and independent consultant.

Description: This research report addresses two purposes: [1] to incorporate phase transitions into a copula framework, and [2] to calculate the probability of defaulting in the context of a portfolio of correlated mortgages when a contagion effect exists. Understanding and measuring the effects that contagion has on the probability of defaulting, for both single assets and portfolios of assets, are crucial to collateralized debt obligation [CDO] markets.