Explainable machine learning for actuaries

This webinar explores how Behavioral Economics can bridge the gap between complex financial products and actual consumer decisions within the pension and insurance industries. By shifting the focus from purely technical designs to the cognitive reality of "System 1" and "System 2" thinking, we will analyze why even the most robust actuarial models often face inertia and low uptake. The presentation moves beyond theory to examine proven strategies from leading global markets who have successfully maximized participation and savings rates. We will discuss how to apply Choice Architecture to overcome deep-seated biases that hinder effective risk protection across both retirement and insurance sector. Ultimately, this talk redefines the actuary's role as a "Decision Architect," capable of building scalable infrastructure that is both mathematically sound and humanly effective in a modern regulatory landscape.


Speaker: Diego Valero Carreras
Session moderator: Abraham Hernández Pacheco

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

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 session provides insights from both life insurance and property & casualty perspectives.

Speaker: Ralph Blanchard and Tamara Burden
Moderator: Taiga Yokoyama

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

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