Description: Uncertainty of a predictive model is a fact of life that many insurers could be overlooking at their peril without a framework for assessing it. The article provides a framework to understand the prediction uncertainty, which can be critical for the insurance operations using predictive models. Coin toss example explains model uncertainty and randomness inherent in making predictions to the number of heads expected. A mortality analysis case study illustrates how the Bayesian methodology can be used to develop the prediction uncertainty. In particular, the article shows that the Bayesian framework is a way to combine historical data and actuarial judgment, and directly address the model uncertainty through its posterior distribution.

Description: Read the May 2020 issue of Actuarial Technology Today 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: Description of SOA's initiative in predictive analytics

Description: Summary of the first annual predictive analytics (PA) symposium specifically targeted to help actuaries get into PA & to take it to the next level of expertise.

Description: Results of a survey of healthcare organizations regarding use and plans for Predictive Analytics

Description: The artcle looks at the most important features necessary to conduct a meaningful quantitative analysis. The article also discusses how predictive analytics should be used as a tactic to anwer question and not an end in of itself.

Description: A discussion on how the look of charts and graphs can affect comprehension of data.

Description: This article discusses ethical issues that have arise with advancements in Data Science. Through examples of these issues address actuarial professionalism.

Description: The article warns actuaries that using predictive models in the underwriting process has a strong potential of inadvertently using variables that are prohibited by law.

Description: This practical session would provide a technical overview about Generalized Linear Model (GLM) and describe how GLM can be applied as a business tool in life insurance. The session would also focus on case-studies sharing across different functional areas such as pricing, fraud detection, underwriting, product development and other real-world applications.