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.

Description: "Artificial Intelligence can bring many benefits to the insurance business, but also many risks that need to be identified and managed. In this video, Dave Ingram FSA MAAA, a SOA Board member and host of the podcast Crossing Thin Ice, discusses some of the common applications and risks of AI for insurance operations and actuarial work. He also shares his top 10 AI risks and some tips for finding the right balance between overconfidence and fear when adopting AI. Contributors: Dave Ingram FSA, MAAA ; Chris Lombardi FSA, MAAA, and Jon Forster ASA, MAAA "

Description: This video by Arthur da Silva, FSA, FCIA introduces the core concepts of cloud computing and cloud platforms, and explains the benefits and trade-offs of using cloud-native, cloud-enabled, and cloud-based applications. It also provides an overview of the current and future usage of cloud computing among actuaries. The video is aimed at actuarial professionals who want to learn more about the cloud and how it can enhance their work.

Description: This video covers the essentials of model validation and measures of accuracy in actuarial AI models. You'll learn about the steps, techniques, and tools involved in validating and measuring the accuracy of AI models in actuarial work. You'll also see a practical case study that demonstrates how these concepts are applied in a real-world scenario. Contributors: Chris Smith ASA, MAAA; Henry Chen FSA, MAAA; Jon Forster ASA, MAAA

Description: Uncertainty of a predictive model is a fact of life that many insurers could be overlooking at their peril without a framework for assessing it. The article provides a framework to understand the prediction uncertainty, which can be critical for the insurance operations using predictive models. Coin toss example explains model uncertainty and randomness inherent in making predictions to the number of heads expected. A mortality analysis case study illustrates how the Bayesian methodology can be used to develop the prediction uncertainty. In particular, the article shows that the Bayesian framework is a way to combine historical data and actuarial judgment, and directly address the model uncertainty through its posterior distribution.

Description: This video explains how cloud computing and APIs can help deploy AI solutions and their impact on the actuarial profession. It compares the old and new world of actuarial modelling using these technologies and discusses what actuaries should know about them. The video is presented by Arthur da Silva, FSA, MAAA the VP of Actuarial at Slope Software, a cloud-native actuarial platform.

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: In this video, Chris Smith ASA MAAA, a health actuarial consultant and data science enthusiast, introduces the basics of machine learning and its applications in actuarial science. He covers various techniques, such as unsupervised, supervised, and reinforcement learning, as well as neural networks and gradient boosting. He also discusses the potential risks and ethical considerations of using these advanced technologies in the actuarial field. Contributors: Chris Smith, ASA, MAAA, Jason Reed, FSA, MAAA, Mitchell Momanyi FSA MAAA, Jessica Calafell, FSA, MAAA; Jon Forster ASA, MAAA