Description: In this video, Joe Long ASA, MAAA, a consulting actuary and data scientist at Milliman, explains the concept of the bias-variance trade-off and its connection to actuarial credibility theory. He demonstrates how machine learning techniques like penalized regression can help actuaries balance bias and variance in their projections, using long-term care experience studies as an example. He also discusses the broader applicability and future directions of integrating advanced analytics into actuarial practice. Contributors: Joe Long ASA, MAAA; Jess Calafell, ASA, MAAA; Jon Forster ASA, MAAA

Description: In this video, Jing Lang FSA, FCIA, MAAA, FLMI provides a panoramic view of the history and evolution of Artificial Intelligence (AI), from its theoretical origins to contemporary breakthroughs in deep learning, autonomous vehicles, and natural language processing. It highlights key milestones such as the Turing Test and the development of chatbots such as ELIZA, while emphasizing the ethical considerations that come with AI advancement, including privacy, bias, and transparency.

Description: "This video features Jing Lang, FSA, FCIA, MAAA , FLMI. Jing is an AI expert and Actuary with over 15 years working in the Insurance Industry. Here he provides an insightful overview of Artificial Intelligence (AI) and its various manifestations, from generative AI generating divers content to everyday applications like smart speakers and self-driving cars. It distinguishes between Artificial General Intelligence (AGI), which can perform tasks akin to humans, and Artificial Narrow Intelligence (ANI), which focuses on specific tasks.The narrative delves into key AI subfields like Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI), explaining their functionalities and differences.

Description: Generative AI is a frontier in artificial intelligence that can create new data from existing data sets, with applications in risk modeling, product development, data analysis, and more. This video presented by Chris Smith, ASA, MAAA introduces the fundamentals, technologies, and implications of generative AI for actuarial science, as well as the ethical considerations and limitations that actuaries must address. Learn how to harness the power of generative AI ethically and innovatively in your actuarial practice. Contributors: Chris Smith, ASA, MAAA; Jason Reed, FSA, MAAA; Sam Behrend, FSA, MAAA ; Jessica Calafell, FSA, MAAA; Jon Forster ASA, MAAA

Description: In recent years, vast amount of data from emerging experiences has helped insurers gain a deeper understanding of their policyholders and the factors driving their behaviors. However, due to limitations in traditional experience study methods, this understanding typically remains at the cohort level. This series of articles proposes a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.

Description: You’ve built or updated a predictive model. Is it any good? How can you interpret and understand its predictions? How does it compare to alternative models? This session will look at traditional and non-traditional ways of validating predictive models, with examples from case studies.

Description: We compare two predictive analytics methods - GLM and GBM in terms of predictiveness vs. interpretability.

Description: Highlights of two 2018 health events organized by the Colombian Association of Actuaries and sponsored by the Colombian Government. Seminar on Risk Adjustment and Predictive Modeling in Health. Seminar on Risk Management for the Health Insurers

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.