GET BETTER ACQUAINTED WITH YOUR KNOWN UNKNOWNSGET BETTER ACQUAINTED WITH YOUR KNOWN UNKNOWNS 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 ...
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.Hide
- Authors: Dan Kim, Boyang Meng
- Date: Apr 2020
- Competency: Results-Oriented Solutions
- Publication Name: Predictive Analytics and Futurism Newsletter
- Topics: Experience Studies & Data>Mortality; Predictive Analytics>Modeling techniques; Predictive Analytics>Quality control & model governance