Events Library

Motivated by insurance applications, we propose a new approach for the validation of real-world economic scenarios. This approach is based on the statistical test developed by Chevyrev and Oberhauser (2022) and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to a variety of stochastic processes exhibiting different pathwise properties (Hölder regularity, autocorrelation, and regime switches) and which are relevant for the modelling of stock prices and stock volatility as well as of inflation in view of actuarial applications.


Speaker: Hervé Andrès
Session Moderator: Łukasz Delong

Classical public pension schemes combine usually PAYG mechanism and Defined Benefit architecture. Aging threatens clearly this kind of structure and motivates the development of hybrid solutions mixing on one side PAYG and Fully funding and on the other side DB and DC (Defined Contribution). The purpose of this presentation is to develop some of these hybrid plans and introduce actuarial tools in order to implement inter-generational and intra generational fairness and sustainability in these public programs.

Speaker: Pierre Devolder
Session Moderator: Jennifer Alonso Garcia

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

AFIR-ERM: A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada

There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects.

In this webinar, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States. The underlying flood occurrence model quantifies the financial impacts of short-term precipitation dynamics under present and future climate conditions by leveraging statistical, machine learning, and climate model data. The model is designed for applications that do not require street-level precision, as is often the case for scenario and trend analyses.
Our analyses show that climate change and urbanization will typically increase losses across Canada and the United States, while impacts remain strongly heterogeneous across regions. Portfolio applications highlight the importance for (re)insurers to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

Speaker: Mathieu Boudreault
Moderator: Łukasz Delong

Climate data is everywhere. Accessible climate data for actuarial modeling, not so much.
Building on the American Academy of Actuaries’ paper "Climate Data: Actuarial Perspectives on Quality, Challenges, and Effective Risk Quantification", this webinar explores the current landscape of climate data through the lens of various actuarial practice areas. The presentation highlights significant issues regarding data quality and availability, specifically addressing blind spots such as the reporting lag bias in Property & Casualty loss data and the complexity of attributing Life and Health experience to climate changes or weather events. By identifying these cross-practice data gaps, the discussion focuses on potential approaches for improving data accessibility, integration, and usability to support future pricing, reserving, and effective risk quantification.

Speaker: Peter Ott
After a brief stint as a high school math teacher, Peter Ott has been in the insurance and reinsurance industry since 2012. Over his career he has worked on admitted lines, excess and surplus products, and reinsurance. His background includes traditional pricing, catastrophe modeling, data management, stochastic reserving, and actuarial research. In his current role, he serves as Director at Howden Re.  Peter is the current Vice-Chairperson for the Climate Change Joint Committee and Chairperson of the Actuaries Climate Index Work Group (ACI/ACRI) of the American Academy of Actuaries.

Speaker: Daniel Pribe
Daniel Pribe is a seasoned actuary and industry leader with expertise in balancing risk and reward, particularly within the healthcare sector. He founded Iris-Edge as a boutique consultancy dedicated to delivering focused, data driven insights that provide clients with a competitive edge. He has over 35 years of experience working in the health care field with extensive experience and expertise in strategic planning and forecasting, Value-Based-Care contracting, contracting, experience and ROI analyses, and medical plan design and pricing for MA, Commercial, and Medicaid lines of business.  Dan has written or co-written several articles for the SOA and the American Academy of Actuaries and has spoken on various healthcare related topics such as healthcare reform, climate change, and end of life care in an aging world. He is currently a member of the Society of Actuaries Board of Directors. Dan received his Bachelor of Science degree in Mathematical Statistics from The Ohio State University. He is a Fellow of the Society of Actuaries and a Member of the American Academy of Actuaries.

Moderator: Marc Slutzky
Marc Slutzky an actuary with more than 50 years of experience. At the present he is a Board Member of IACA and is a Member of the Board of Trustees of The Montclair Foundation. During his career he was involved in advising insurers on capital management, mergers and acquisitions and on regulatory and tax issues. He has worked at the Equitable Life Insurance Company, the Home Life Insurance Company and most recently spent 23 years as a Principal of Milliman, Inc. He is a Fellow of the Society of Actuaries, a member of the American Academy of Actuaries and a Chartered Enterprise Risk Analyst.

Amidst the recent flurry of activity around AI, actuaries will be in good company as they consider how to extract the benefits of AI advancements, while avoiding their downside risks. Actuaries that develop and deploy AI systems must consider how to deliver the potential gains in accuracy and speed, while avoiding risks like biased decision-making. At the same time, they must carefully consider how AI systems will integrate into employee workflows. Responsible AI and human-centric AI offer valuable guidance to address these issues and can assist actuaries in realising the benefits of AI.


Speaker: Maura Feddersen
Moderator: Ernst Visser

In this webinar, we explore a non-cooperative optimal reinsurance problem under likelihood ratio uncertainty, aiming to minimize the insurer's worst-case retained loss. We relate the optimal reinsurance strategy under the reference measure to that in the worst-case scenario, with generalizations to insurance design problems using tail risk measures. We also identify distortion risk measures where the insurer’s optimal strategy remains unchanged in the worst-case. Using an expectile risk measure, we determine optimal policies for the worst-case. Additionally, we examine a cooperative problem as a general risk-sharing model between two agents in a comonotonic market, comparing outcomes with the non-cooperative case.


Speaker: Ziyue Shi
Moderator: Brian Fannin