Events Library

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

AFIR-ERM Webinar: Optimal Preventive Strategies in Insurance Risk Models. We develop an integrated framework for optimal risk management in insurance that considers preventive strategies and/or risk sharing in risk models. We odel an insurer facing risks whose frequency and severity can be influenced by preventive efforts, as well as by exogenous risk-sharing instruments. Within a dynamic risk model, we formalize the insurer’s problem as either minimizing ruin probabilities or maximizing a mean–variance criterion for the terminal surplus process, while accounting for the implementation costs of preventive measures and the cost of risk transfer. We derive analytical conditions characterizing the optimal prevention efforts and optimal risk-sharing structure, and we study how these choices depend on risk characteristics. Numerical illustrations based on classical risk models show that jointly optimizing prevention and risk sharing can substantially reduce ruin probabilities compared to treating these instruments in isolation.


Speaker: Li Jingchao
Session moderator: Guangyao Liu

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.