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: It talks about the major areas of data science and how to learn data science with the actuarial background.

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

Description: Defines Accelerated Underwriting programs as a transformational trend, and outlines a process for evaluating mortality implications of such programs.

Description: An introduction of predictive modeling techniques that are applicable to insurance industry, including generalized linear model (GLM) and classification and regression tree model(CART). Other models such as data clustering, neural network, and support vector machine (SVM), are also briefly discussed.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.