Description: This article takes a comparative review on Medicare and Private Health Insurance and examines the cost of administration. From Health Watch Newsletter, May 2006, Issue No. 52.

Description: This article provides the reader with some insights regarding tax planning under the new required methodology.

Description: This article discusses risk transfer and how difficult it is to define and describe. For actuaries, the challenge is to develop an analytical framework under which the presence of insurance risk can be identified and assessed.

Description: A discussion of the effective dates and statutory reserves cap for variable annuities under Actuarial Guideline 43.

Description: Feature article that contends in order for actuaries to thrive, technology needs to be central to company strategy and actuaries should view coding as a core competency—equal to or more important than other “traditional” skills.

Description: LDTI’s introduction has forced many insurers to examine their existing operations and technologies. They must determine how to optimize compliance efforts while balancing data, analytics and system modernization considerations to achieve maximum value. This article will examine the four key facets—the data, technology, processes, and people—essential to any successful LDTI exercise. It will also touch on the various approaches for tackling LDTI and how a modernization mindset can help insurers realize much greater transformation for the business.

Description: This article describes how to externalize code of Moody's Axis platform to Visual Studio, so that all the actuarial calculation code run through corporate DevOps pipeline. This article gives specific examples of the author has used in his job to accomplish automated model governance.

Description: Machine learning models are not black-boxes; these models can easily be interpreted using tools like SHAP, LIME and Anchor. These tools can facilitate the understanding of the model and the business problem. We can used these tools to build better models and communicate how predictions are generated. This article demonstrates their use in a mortality model.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.

Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.