Description: Using the Cox proportional Hazards Model to improve underwriting debits and credits.

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: Identifies current trends and considerations when setting the mortality improvement assumption for individual life insurance in the US.

Description: The article is Part 1 of two articles that provide a case study for the development of a profitability and risk dashboard. The sample dashboard provides distilled information regarding actual to expected (A/E) ratios for key risks, agent quality scores and customer value scores. The dashboard would also provide the value of new business written, surplus strain, debit balances, and expected losses on debit balances at different levels, down to the agent level. A product mix index is provided, together with visual comparisons of key metrics to the company's business plan. The Part 2 article will add information to the dashboard regarding risk margins, target capital, and company value.

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

Description: The article explores the possibility of using the emerging technology for life insurers to build a monitor system for better risk and profitability management.

Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.