Description: Actuaries have the opportunity to transform to a new role that utilizes predictive analytics and new data sources such as epigenetics, to better humankind.

Description: How actuaries can become comfortable accessing, integrating and building complex data sets from big and broad sources of information to study the value of multidimensional drivers of change.

Description: We highlight a new approach leveraging predictive analytics to identify high risk and rising risk opioid users for more effective screening and follow-up care.

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.

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

Description: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.

Description: The article focuses on the use of predictive analytics in the development of experience studies, pros and cons of using predictive analytics versus traditional techniques, and the implications for setting assumptions and improving profitability.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.