Convergence of Capital and Insurance Markets: Pricing Aspects of Industry Loss Warranties

Why use a Chain Ladder when you have Golden Stairs? The Benktander method has been Professor Walther Neuhaus’ loss reserving method of choice for more than three decades, due to its simplicity and efficiency. To him, it's the Benktander Golden Stairs. This talk gives a mathematical formulation of the notion that the Benktander method is not too far from optimal, most of the time. It does not claim that the Benktander method is optimal, only that it often is close; in most situations it outperforms the Chain Ladder method with a good margin. The criterion used to compare methods is “weighted average relative efficiency”. The results dispel the idea that iterating the Bornhuetter-Ferguson method more than once leads to a significant improvement in prediction accuracy.

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

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

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.

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.

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