A Stochastic Framework for Incremental Average Reserve Models

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.

IAA's CAT Modelling Task Force facilitated the webinar with Verisk and Moody's examine the evolving landscape of climate risk and catastrophe (CAT) modeling, exploring how advancements in modeling methodologies are shaping risk assessment across the insurance and reinsurance industry.

In the first part of this webinar, we will analyze the effect of using weighted maximum likelihood estimation for survival models where the assigned weights depend on the sum assured – a common practice in life insurance to focus on the financial consequences of survivorship. In this context, we consider how these estimates may change when more granular mortality models are used that are based on the socioeconomic characteristics of policyholders. During the presentation, we will highlight several practical considerations for developing weighted models for liability estimation and discuss the impact of such approaches on annuity liabilities. To illustrate this, we will present results using a real-world insurance dataset providing survival information on individuals who purchased annuities between 2015 and 2022 in the Netherlands. The analysis will include a financial test of fit to evaluate the performance of models. In the second part of the webinar, we will discuss research findings on the potential influence of socioeconomic factors on annuitants' longevity, based on publicly available, area-level indicators. The analysis will present detailed results on variables that could be useful for modelling policyholder mortality in the Netherlands, and will compare these with the traditional approach of using age, gender and pension information as covariates.


Speaker: Andrey Ugarte Montero
Andrey Ugarte Montero is a postdoctoral researcher at the Research Center for Longevity Risk (RCLR) of the University of Amsterdam. He holds a master’s degree in Actuarial Science from the University of Lisbon in Portugal and a PhD in Actuarial Science from HEC Lausanne in Switzerland. His interests include actuarial modelling in life and health insurance and the financial consequences of longevity within an insurance context. He is also interested in the applications of machine learning and data science in the insurance sector. From December 2013 to August 2017, Andrey worked as an actuarial consultant for the firm EY (Ernst & Young) in Latin America. He’s also pursuing certification as an Associate/Fellow of the UK’s Institute and Faculty of Actuaries


Moderator: Ernst Visser
Ernst is a Dutch actuary working for KPMG Financial Risk Management in The Netherlands. He has a master degree in physics and a master degree in actuarial science. In 2025 Ernst obtained an MBA degree with a thesis investigating the potential for disruption in the insurance industry. Ernst is a fully qualified member of the Dutch actuarial society. He has Over 30 years experience in consulting in the insurance industry