An Actuary’s View on Model Uncertainty

International Actuaries Day 2026: 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗰𝗿𝗼𝘀𝘀 𝗕𝗼𝗿𝗱𝗲𝗿𝘀: 𝗚𝗹𝗼𝗯𝗮𝗹 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗔𝗰𝘁𝘂𝗮𝗿𝗶𝗮𝗹 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻

Predicting and understanding solvency figures poses major challenges for life insurers. Internal risk models provide realistic representations, but they remain company-internal and make model understanding difficult. This makes it hard to compare new machine-learning methods for prediction and explainability objectively and under realistic scenarios. We developed openIRM, an open-source internal risk model for an artificial life insurer. It combines an economic scenario generator based on the G2++ interest-rate model with a cash-flow projection model. The model supports both outer real-world simulations and inner simulations for determining basic own funds. It has also been calibrated for all trading days from September 2016 to December 2023 and allows the use of direct and indirect valuation methods, which have been shown to produce consistent results. This talk gives a short introduction to risk modelling for a life insurer under Solvency II, outlines how the openIRM model works, and demonstrates its practical use through a concrete example. Speaker: Mark-Oliver Wolf Moderator: Ernst Visser

Individual loss reserving methods have undergone substantial development in the past decade, driven by increased access to granular-level insurance claims data. This research aims to propose a micro loss reserving model tailored for multi-coverage insurance policies, where a single insurance claim might trigger payments from multiple coverage types. A copula-based multivariate regression approach is employed to jointly model the settlement time and loss amount, effectively capturing the dependence among various types of loss amounts and their correlation with the settlement time. We stress the importance of considering both types of dependence for accurate reserving prediction and uncertainty quantification. Furthermore, we propose computationally efficient algorithms for parameter estimation and dynamic prediction. Through numerical experiments and real data analysis, we demonstrate the effectiveness of the proposed multivariate predictive model in loss reserving applications. Speaker: Roxane Turcotte Moderator: Joana Gregório Raposo

This webinar was presented by IAA's Inclusive Insurance Virtual Forum (IIVF) which explained approaches to assessing the value of inclusive insurance from the perspectives of various stakeholders, including insurers, regulators, and development practitioners. Speakers discussed the range of factors that influence the sustainability and effectiveness of inclusive insurance initiatives, covering financial, economic, social, and development-related considerations.

Emerging markets are at the forefront of innovation in sustainable insurance, with actuaries playing a central role in shaping this evolution. From parametric covers protecting Kenyan herders, to sovereign catastrophe bonds in Mexico, to ESG and climate-risk integration across African and Asian insurers - these markets are generating new models for climate resilience and financial inclusion. Drawing on case studies from Africa, Asia, the Caribbean, and Latin America this session will examine the expanding remit of the profession in addressing some of society's most pressing challenges.


Speaker: Arpita Das
Moderator: Peng Shi, ACAS, FSA, PhD

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.