Description: The Modeling Section, Predictive Analytics and Futurism Section, Committee on Life Insurance Research, Product Development Section and Reinsurance Section releases a report that can help to educate actuaries on how best to implement predictive modeling into relevant areas of actuarial practice. This report includes a review of existing literature and current industry practices, as well as a comprehensive set of considerations for predictive modeling in insurance applications.

Description: This project obtains ensemble simulations of surface atmospheric temperature over North America.

Description: Overview on current trends in medical practice impacting the LTC insurance business with a focus on dementia, risk stratification, and predictive modeling.

Description: History of Big Data, and approaches to cope with it. The author suggests that actuaries have been using similar methods for a long time; but need to get up to speed on the newer terms and variations.

Description: Discussion about the need for, and approach to, measuring organizational expertise ton ensure the learning investment is resulting in expertise growth.

Description: Abstracts of recent articles of interest to health actuaries

Description: Read the April 2019 issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section of the Society of Actuaries.

Description: Describes the predictive analytics revolution taking place in the insurance industry

Description: A concise overview of Generative Adversarial Networks.

Description: Predictive models can improve the traditional actuarial assumption-setting process. Data science techniques can provide deeper insights into experience through their ability to identify key drivers, rank importance and account for interactions. The presenters will cover ways in which companies can utilize predictive analytics to set assumptions, including the various methods available and practical approaches for implementation. They will also discuss the advantages and challenges of using predictive models for setting assumptions for life, annuity and disability products.