Description: The author reviews the impact of health care exchanges becoming operational in 2014, and he offers ideas for efficient risk identification and ways to measure the results.

Description: This article includes a description of robotic process automation as it relates to actuarial departments. Robotic process automation is becoming more prevalent in actuarial departments as different regulatory changes cause the processes to be more complex. As robotic process automation takes over more jobs, actuaries will have to enhance and evolve their skillsets to better complement the new tools. This article provides an introduction for actuaries to inform them what they should expect, and how they can adapt to these changes.

Description: The speaker will discuss the training of neural networks to compute future Greeks of liabilities (or options) without the use of nested stochastics.

Description: SOA interview with David Axene

Description: The April/May 2013 edition of The Actuary magazine—the Society of Actuaries’ flagship publication.

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: Interview of people knowledgeable about risk adjustment and the use of social determinants of health with that context for Medicaid.

Description: Regulatory change and increased focus on internal risk management are driving a renewed interest in model efficiency. In this session, we will review several new and existing tools, that show significant promise for advanced modeling of equity linked products. These include: • Clustering • Use of unique--and potentially a different number of--scenarios for each policy • Semi Monte Carlo (SMC) – this is a proprietary technique that is similar to a combination of Monte Carlo and numeric integration • Using a neural network approach to optimize some of the more complex and manual aspects of clustering and improve the fit of the clustered model • Combining SMC with the use of unique scenarios per policy

Description: Learn industry best practices including challenges faced by retirees, such as needing exposure to growth, most exposed to sequencing risk, and the need for smooth returns to avoid poor investor behavior caused by risk aversion. Other topics will include how to set investment objectives which consider the members’ real-world needs and how to build portfolios and implement risk management strategies to meet those objectives. The speaker will provide real-life examples/case studies.

Description: This article introduces IT DevOps to actuaries and how it is applicable to them to improve their modeling efforts and make them more efficient.