Tres actuarios de diferentes culturas y países. Conversan sobre su experiencia profesional y laboral; comparten puntos de vista, perspectivas del mercado, cultura laboral, herramientas de trabajo, entre otros. El evento será en español.
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.
This webinar is based on a paper published in the ASTIN Bulletin.
The IAN illustrates how actuaries may apply professional judgment in activities such as selecting data, addressing data limitations, choosing methodologies, setting assumptions, interpreting results, and communicating outcomes.
This webinar, hosted by the IAA’s Professionalism Committee, will introduce the key concepts in IAN 300, and discuss how the Note can support actuaries in applying sound professional judgment in their day-to-day practice.
This session brings together actuarial and machine learning perspectives to explore how AI can support reserving practices.
Due to unforeseen scheduling issues, we have had to adjust the start time of our upcoming webinar. We kindly ask that you take note of the new start time and join us accordingly:
New Webinar Start Time: 8:30 EST |2:30 PM CET 04 March
We apologize for any inconvenience this may cause and appreciate your understanding. We look forward to your participation.
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.
PBSS: 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. Don’t miss this opportunity to learn from one of the field’s most influential voices.
PBSS: Risk Sharing Mechanisms in PAYG Public Pension Schemes
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.
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.
This webinar is being offered in two separate sessions to accommodate different time zones. Please be sure to register for the session that best aligns with your local time zone to ensure you can attend comfortably.
In 2024 the International Actuarial Association established a Task Force to assist in the profession's response to Artificial Intelligence and its impact on the profession.
This webinar is being offered in two separate sessions to accommodate different time zones. Please be sure to register for the session that best aligns with your local time zone to ensure you can attend comfortably.
In 2024 the International Actuarial Association established a Task Force to assist in the profession's response to Artificial Intelligence and its impact on the profession.
This is a joint webinar between AFIR ERM Section and Enterprise and Financial Risk Forum.
Liquidity risk arises from the inability to meet cash demands (e.g., from claims or policy surrenders) from readily available funds. While liquidity needs are normal for any business, insurers are also exposed to stress liquidity risk, such as from mass policy cancellations and from catastrophes.
This webinar focuses on how companies might prepare for and manage liquidity during events that cause significant unexpected liquidity stress. The webinar first addresses liquidity risk and liquidity risk management from the life insurance perspective and then addresses these areas from the property/casualty perspective.
This webinar hosted by the IAA Data Analytics Virtual Forum (DAVF) which will explore how data and AI regulations are developing across key regions worldwide, highlighting similarities, differences, and emerging governance trends. Speakers will present regulatory perspectives from the EU, UK, Asia, and North America, offering insights into how each jurisdiction is approaching responsible AI, data protection, and sector specific oversight.
By bringing together these global viewpoints, the session will provide attendees with an understanding of the evolving regulatory landscape and its implications for professionals working with AI and data driven technologies.
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
