Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry

Description: Traditional risk scoring models include only fixed (non-random) demographic and condition-based variables. By including the provider effect as part of the risk scoring model as a random effect, we can get better estimates of provider performance (incorporating practice patterns conditional on patient morbidity profile). This approach can be generalized to study other factors that are now masked in error variance (i.e. area, Social Determinants) but if modeled could give us better estimates of risk

Description: A look at the near future and far future of various elements of Actuarial Models as defined through ASOP 51. What is driving the complexity for inputs, calculations, and outputs. Howe will actuaries handle these challenge?

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

Description: Early in his career, Ehren Nagel, FSA, MAAA, scoured through podcasts and articles about predictive analytics, long suspecting this was a field with potential for him.

Description: This article provides an overview of activities that should be considered by an actuary when validating a predictive model. It describes several components to consider when validating a predictive model and provides an examination of various methodologies that can be implemented in the validation process.

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.

Description: Actuarial concepts of credibility can be extended to encompass additional information provided by new machine learning estimates. New statistical ideas also indicate how to generalize to blend arbitrarily many estimators, while not double-counting common information. For example, historical claim costs and risk scores both leverage diagnosis code information. We introduce two new estimators, one based on partial correlation and the other based on the machine learning-derived Shapley statistic, to address these limitations.

Description: An introduction to the methods and parameters for Bayesian Hyperparameter Optimization in a Life Insurance context.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.