Yes we CANN!: Machine Learning in Actuarial

This webinar introduced the IAA AI Task Force and provide an overview of the “Case Studies & Tools” workstream and its objectives.

The session includes a walkthrough of the Task Force's GitHub account and repositories covering how they are structured, how they function, and how you can explore and contribute to them. A curated collection of GitHub case studies is then presented, with a detailed walkthrough of one fully developed case study illustrating how AI can be applied to a real actuarial problem.

The webinar also touches on the GitHub tools repository showcasing the AI tools through short presentation videos. a more detailed walkthrough of developed case study to illustrate how AI can be applied to a practical actuarial problem.

With the advancement of technology, insurance companies are increasingly adopting usage-based insurance (UBI) supported by vehicle telematics. Vehicle telematics refers to data collected from in-vehicle sensors or smartphone applications during driving, such as speed, acceleration, braking, and steering. It provides a rich, high-frequency record of how a vehicle is driven, offering insights into driving habits, behaviour, safety, and potential risk. However, many current approaches rely on aggregated metrics and do not fully capture the detailed time-series patterns in telematics data. This presentation introduces a flexible framework based on a continuous-time hidden Markov model (CTHMM) to analyze trip-level telematics data directly. Our approach avoids predefined thresholds for harsh events or assumptions about accident probabilities, and uses only telematics data, requiring no traditional demographic covariates. Using an unsupervised anomaly detection technique, we identify deviations from normal driving patterns linked to higher accident risk. The framework is tested on both controlled and real-world datasets, and the results reveal clear behavioural differences between drivers with and without claims, offering practical insights for insurance, accident analysis, and prevention.

Explore how data and AI regulations are evolving across the globe in this insightful webinar hosted by the IAA Data Analytics Virtual Forum (DAVF).

This session brings together perspectives from the EU, UK, Asia, and North America, highlighting similarities, differences, and emerging trends in areas such as responsible AI, data protection, and sector-specific oversight.

Speakers: Alex Waite, Amanda Hug, Esko Kivisaari and Yosuke Fujisawa
Moderator: Kim Steiner

In 2024, the International Actuarial Association established a dedicated Task Force to guide the actuarial profession's response to Artificial Intelligence. After two years of work spanning governance, education, professionalism, and the evolving role of the actuary, the Task Force is proud to present three foundational papers on the responsible use of AI in this webinar.

Speaker: Bernice Lim, Chadwick Cheung, Dorothy Andrews
Moderator: Peter Withey

Unlock how Artificial Intelligence is reshaping pensions and social security work. Join us for an insightful session featuring leading actuarial AI expert Ronald Richman, who will share practical applications of AI in longevity modelling, experience analysis, and actuarial workflow automation. This webinar will highlight real-world use insights, emerging opportunities, and what actuaries doing work in pensions and social security need to know to stay ahead in an AI-driven future.

Be sure to join us for what promises to be an informative and engaging discussion.

Speaker: Ronald Richman
Moderator: John Anderson

This session brings together actuarial and machine learning perspectives to explore how AI can support reserving practices.

The actuarial perspective will cover the practical limitations of current reserving workflows, where judgment enters traditional methods and why consistency is difficult, and what actuaries need from AI, including transparency, validation, and governance. It will also include a case study comparing traditional results with AI-supported modeling and how ML results can serve as a second-opinion framework.

The machine learning perspective will cover the Bayesian ML framework, including its architecture, model selection based on predictive power and cross-validation, and how explainability is built into the model. It will also discuss why full distributions matter more than point estimates and share technical lessons from applying ML to insurance triangles.

Speakers: Ben Zickel and Yulia Nechay Moderator: Joana Raposo