Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: Editorial for the May 2019 issue of Predictive Analytics & Futurism newsletter.

Description: A discussion on how the look of charts and graphs can affect comprehension of data.

Description: Technology has streamlined countless processes in our daily lives, and insurance is no stranger to this transformation. Technology can make insurance simpler and more personal for consumers, and can enable actuaries to better analyze unstructured data, make good decisions, create new products, and find channels to enhance profitability, provided they can tame the flood of information pouring in from across the digital ecosystem.

Description: The advent of machine learning algorithms has taken predictive analytics and risk scoring to a new level. From gradient boosting machines to k-nearest neighbors regression, the use of these techniques has gained significant traction in healthcare - including health systems like the Cleveland Clinic. This session will discuss the use of predictive analytics to go beyond just assessing the risk of a patient, and instead focus on prescribing a specific intervention of care. In practice, the recommendation of a care plan goes beyond just attributing a score to a patient. It provides physicians and care coordinators the most probabilistic options - e.g. referral to social worker, depression screening, weight-loss program - for positively impacting a patient's future healthcare outcome.

Description: This article will highlight new model visualization approaches we use to shed light on the “black-box” nature of advanced modeling techniques. These visualizations help us better highlight and understand the key drivers of predictions so that they can be understood and validated by all stakeholders

Description: The presenters will share information about: -Insights into causes of death through the use of predictive models, based on a reinsured U.S. life portfolio, using logistic regression models to understand three main causes of death: cancer, heart and external causes; -Opioid abuse is a widespread phenomenon with drug-related mortality rates rising steeply in the U.S. over the past two decades. But what does the most recent data tell U.S. about the outlook for Opioid-related mortality?; -'Deaths of despair' have been publicized as a cause for the poor performance of U.S. mortality in recent years. The presenters will be looking at the different causes under this label, their evolution and explore the drivers behind them.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: Health claims are not normally distributed. Claims Forecasting can be improved by understanding the entire distribution of plausible values of future claims, for example knowing there is an 80% chance future claims are between $340 and $375 PMPM. Confidence intervals around our estimates allow us to understand for each range around the best estimate (for example +/- 1%) what is the probability that future claims will fall in that range. Quantile Regression is a mathematical technique where you can estimate any percentile of the claims distribution you are looking for as a function of demographics, diagnosis indicators, prior cost variables, or other significant predictors. You can see which coefficients are important at different parts of the distribution. Individual and group distributions can be constructed. There are numerous applications in pricing and underwriting, stop loss, provider profiling, ACO target setting, allocation of group risk charges based on volatility, discounting strategy, and experience analysis.