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: 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

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, Dave Ingram FSA MAAA, a SOA board member and podcast host, discusses the ten AI risks that insurers face and the specific mitigations that can be used for each of them. He also explains the importance of having an AI Use Policy that covers various aspects of data governance, ethical guidelines, transparency, fairness, human oversight, and compliance. He concludes by noting that AI will evolve and insurers will need to monitor and remediate their AI applications accordingly. Contributors: Dave Ingram FSA, MAAA; Chis Lombardi, FSA, MAAA; Jon Forster, ASA, MAAA

Description: Discussion of machine learning applications currently being used to augment traditional actuarial modeling in the life insurance industry. This includes data science techniques for: Quantifying and explaining differences between model versions or reporting periods Building proxy models for actuarial model output using machine learning One-class classification for data integrity checks Applications of unsupervised learning (e.g., clustering and principal components analysis) to reduce model run time

Description: In this video Jing Lang emphasizes the importance of ethical considerations in AI utilization, particularly focusing on privacy, bias, transparency, and creativity. It highlights the need for collaboration among various stakeholders to address these concerns through regulations, diversity promotion, and ongoing discussions. Sources of bias in AI, such as underrepresented datasets and historical biases, are identified along with strategies to mitigate bias, including careful assessment of inputs and retaining algorithms. Contributors: Jing Lang, FSA, FCIA, MAAA FLMI; Scott Allen, FSA, MAAAA, Henry Chen FSA, FCIA, MAAA; Jon Forster ASA, MAAA

Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry

Description: Deep learning neural network applied to experience analysis for post-level term lapse and mortality.

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: "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.