Description: In this video Jing Lang explores the current and future landscape of AI in the insurance industry, focusing on its impact on actuarial work. She highlights the wide-ranging applications of AI in business, particularly in customer interactions and marketing activities, while also discussing its potential disruptions in the insurance sector. Despite the advancements in AI, Jing emphasizes that certain aspects of actuarial work, such as pricing and reserving, remain largely unaffected for now, but encourages proactive integration of AI tools for future adaptability. Contributors: JIng Lang FSA, FCIA, MAAA, FLMI ; Scott Allen FSA, MAAA; Henry Chen FSA, FCIA, MAAA; Jon Forster ASA, MAAA.

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: 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.

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 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: 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: 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: 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.