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: Health care predictive analytics varies between payers and providers as well as being a powerful tool in business decision modeling.

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: Abstracts of recent articles of interest to health actuaries

Description: This article describes methodologies for detection and deterrence of Medicare fraud. It also describes the validation of PRIDIT for fraud detection in Medicare.

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.

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

Description: The well-known examples of predictive modeling “successes” are numerous. Perhaps less widely recognized is the myriad of ways in which a predictive model can fail to perform as expected, often due to misconceptions or misrepresentations on the part of the analyst. This article, which is intended for a general actuarial audience, focuses on three such pitfalls. By understanding these three common pitfalls, readers may guard against them within their own work, teams, and organizations.