Description: Editorial for the December 2019 print edition of Predictive Analytics & Futurism newsletter describing the large number of informational resources available to PAF Section members.

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: The author presents autoencoders in a clear manner and then goes on to show an intriguing application where an autoencoder can alert us to events out of the ordinary.

Description: The author reviews a book the debunks some of the hype we read about using quantity at the expense of quality. She also points out some unrealistic outcomes when data scientists lack business knowledge.

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means

Description: Contrary to what you might infer from the article title, the author provides counterpoint from some data science skeptics and suggests Big Data sometimes leads to sloppy analysis.

Description: Feature article describing how exponential technologies will affect the actuarial profession.

Description: Reflection on three years on the PAF section council and where we're headed next.