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: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: "Examine the exciting possibilities for artificial intelligence (AI) in health care and its potential to transform the patient journey. You will learn about the various touchpoints where AI is being applied along the patient/care journey, including provider search tools, claims analysis, and fraud detection. By attending the session, you will be able to: Have a comprehensive understanding of the opportunities and challenges presented by AI in health care Evaluate the potential impact of AI on their own organizations Be equipped with strategies for effectively implementing AI solutions and maximizing their benefits for patients, providers, and payers"

Description: This session will present the results of a case study investigating the impact of genetic testing on the reduction of health care costs. This will present both actuarial and clinical analysis of genetic testing in addition to researchers who are applying this approach.

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 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: Model design is an important part of being an actuary, and the SOA highlights the importance of the actuarial control cycle of model design, evaluation and monitoring. What tools are at our disposal for these steps? When should we be concerned or excited about model performance? Can we, and should we, use machine learning tools to assist with model improvement or development?

Description: Volatile interest rates, repositioned investment portfolios, and stress in the banking sector are driving insurers to manage market risk and counterparty credit risk. Our experts in investments, derivatives, and hedge accounting will discuss this new market environment and its impact on hedging. The conversation will include practical case studies, including hedging future bond purchases, hedging floating-rate CLOs, and counterparty credit risk management.

Description: Walk through real-life examples of how artificial intelligence can be used to drive insights and aide actuaries in improving the management of their company’s inforce book of business. A panel of experts will walk through case studies of how, as practitioners, predictive analytics provided solutions to tap further into their data and draw improved insights.