Events Library

This webinar brings together two critical themes shaping the future of actuarial work: model risk management and the ethical adoption of AI. Matthew Byrne opens the session by examining key model risks with a focus on emerging risks from AI and complex models. He shares practical insights from real-world experience on model risk management. The session also discusses the evolving role of actuaries in leading model risk management and ethical AI adoption. Fei Huang follows with a comprehensive framework for ethical AI adoption in actuarial practice, aligned with the AI lifecycle—from problem definition and data collection to modelling, evaluation, and deployment. She maps these steps to the actuarial control cycle and offers practical guidance and research resources for actuaries designing or reviewing data-driven decision-making. Case studies illustrate common pitfalls of AI and black-box models, along with strategies to mitigate risk and support responsible adoption.

As climate risks intensify, the need for timely, scalable, and efficient risk-transfer solutions has never been greater. Parametric insurance is emerging as a vital innovation in the insurance space, offering pre-agreed payouts based on the occurrence of a triggering event, such as a natural disaster or extreme weather threshold, rather than on measured losses. Unlike traditional indemnity-based models, parametric insurance ensures rapid claim settlements and greater transparency, making it particularly suitable for underserved markets and vulnerable populations. This webinar explores the growing relevance of parametric insurance in the context of climate resilience and sustainable development. It will cover the fundamentals of parametric insurance design, how triggers are selected and validated, pricing mechanisms, and the importance of high-quality data. The session will also explore the real-world implications for policymakers, regulators, and insurance providers especially in regions prone to climate-related shocks like droughts, floods, and cyclones. Participants will gain insights into: • What is Parametric Insurance and how it differs from traditional models. • Key design elements: triggers, thresholds, and payout structures. • Opportunities and limitations in deploying parametric products in agriculture, health, and disaster risk financing. • The role of actuaries, regulators, and technology in scaling parametric insurance for inclusive growth. This session is highly relevant for actuaries, development practitioners, regulators, and policymakers seeking to understand how innovative insurance tools can drive resilience and adaptive capacity in vulnerable communities.

Machine learning in claims reserving is an exciting area of actuarial research, with the promise of higher accuracy or greater insight and moving beyond limitations of traditional methods. We make the case for ML in claims reserving, demystify the key concepts, and then provide an update of the recent progress and future goals of the Machine Learning in Reserving Working Party. We aim to leave attendees with a clear understanding of the current state and future direction of ML in claims reserving.