Nos derniers articles

This paper investigates dynamic insurance pricing and risk management when insurers face correlation ambiguity between underwriting and financial investment risks. By employing a robust control framework and G-expectation theory, the research models how insurers make decisions under worst-case beliefs regarding these unknown dependencies. The authors identify five distinct equilibrium regimes, such as pure underwriting or zero underwriting, which shift based on market conditions and ambiguity levels. A key finding challenges traditional assumptions by showing that uncertainty does not always lead to higher premiums or reduced utility for the insurer. Instead, ambiguity aversion can sometimes improve an insurer’s position by encouraging more conservative and robust portfolio allocations. Ultimately, the study highlights that accurately understanding risk dependence is essential for effective regulatory policy and equilibrium pricing in modern financial markets.

This paper provides a rigorous mathematical analysis of the axiomatic foundations used to quantify financial risk. The author traces the evolution of risk measurement from early standards like Value-at-Risk to more sophisticated frameworks including coherent, convex, and spectral risk measures. Central to the text are the representation theorems that define these measures through dual sets of probability scenarios and penalty functions. The scope extends to dynamic settings, where time-consistency is required for multi-period assessments, and systemic risk involving interconnected institutions. Finally, the research bridges the gap between theory and practice by integrating machine learning techniques, specifically examining the concentration of empirical estimators and the use of conformal prediction for distribution-free risk control.

This paper by Caroline Hillairet, Olivier Lopez and Lionel Sopgoui (CREST, UMR CNRS) describes a stochastic SIR model designed to quantify the financial impact of contagious cyber-attacks on corporate revenues and insurance portfolios. By blending epidemiological frameworks with economic granular growth models, the researchers account for the reality that larger firms are more frequent targets and exhibit different internal infection dynamics. The model specifically utilizes Cox-Ingersoll-Ross (CIR) processes to incorporate environmental variability, allowing for more realistic simulations of how ransomware spreads within and between organizations. A key practical application analyzes the 2024 LockBit ransomware attacks, offering insurers a method to calculate Aggregate Exceedance Probabilities to forecast potential losses. Ultimately, the framework bridges the gap between cybersecurity technicalities and financial risk management, providing a tool for measuring systemic cyber threats across diverse industrial sectors.

The paper presents a framework for individual claims reserving based on the projection-to-ultimate (PtU) method as an alternative to the traditional chain-ladder approach. It describes how reserving can shift from aggregate loss triangles to claim-level modeling by directly estimating ultimate claim costs. The approach is presented as compatible with classical actuarial structures while enabling the use of stochastic covariates and machine learning models, including neural networks and transformers. The authors emphasize decomposing reserves into Reported But Not Settled (RBNS) and Incurred But Not Reported (IBNR) components to maintain consistent claim cohorts. Case studies suggest that linear regression can perform robustly in individual-claim settings.

This research explores how enterprise risk management (ERM) can be modernized to combat the rising financial threat of insurance fraud. By integrating artificial intelligence and machine learning into traditional frameworks like Basel II, insurers can shift from reactive investigations to proactive prevention. The author emphasizes the use of data analytics and Principal Component Analysis (PCA) to simplify complex claims data into clear, actionable risk categories. These advanced visualization techniques, such as confidence ellipses and heat maps, allow executives to identify fraudulent patterns and anomalies more efficiently. Ultimately, the paper provides a data-driven roadmap for casualty insurers to strengthen their operational resilience while maintaining regulatory compliance.

Ce rapport officiel de la Caisse Centrale de Réassurance (CCR) détaille l'état du régime d'indemnisation des catastrophes naturelles en France pour l'année 2025. Face à l'intensification des aléas climatiques, tels que les inondations et les sécheresses, le document souligne la nécessité de rééquilibrer financièrement ce système fondé sur la solidarité nationale. Les auteurs présentent quatorze préconisations stratégiques visant à garantir la pérennité du modèle par le renforcement de la prévention et l'ajustement des surprimes d'assurance. Le texte analyse également l'impact de sinistres récents, notamment les cyclones en Outre-mer, pour illustrer les défis croissants liés au réchauffement climatique. Enfin, il réaffirme l'importance du partenariat public-privé pour maintenir une couverture équitable et accessible à l'ensemble des citoyens d'ici 2030.

Ce communiqué de presse de l’AMRAE exprime de fortes réserves concernant la nouvelle contribution de solidarité instaurée par le gouvernement pour couvrir les dégâts liés aux émeutes. Bien que l'association salue l'intégration du dispositif à la Caisse centrale de réassurance, elle dénonce une taxe qui sera inévitablement répercutée sur l'ensemble des assurés, qu'ils soient particuliers ou entreprises. L'organisation critique ce transfert financier qui fait peser sur le secteur privé une responsabilité relevant normalement de l'ordre public et de l'État. Selon l'AMRAE, cette accumulation de prélèvements nuit à la compétitivité des entreprises françaises et réduit la clarté du système de financement des risques. Enfin, l'association appelle à privilégier une stratégie axée sur la prévention plutôt que sur l'ajout de charges financières supplémentaires.

This report examines the expanding natural catastrophe protection gap in Europe, which leaves a significant portion of disaster-related economic losses uninsured. The authors argue that private reinsurers possess the necessary capital, global diversification, and modeling expertise to absorb these risks more effectively than state-led initiatives. They caution that government-backed reinsurance schemes may inadvertently cause market distortions, such as moral hazard or suppressed pricing signals that discourage safety improvements. To enhance societal resilience, the document suggests focusing on increasing insurance take-up rates and implementing stricter land-use regulations. Ultimately, the board advocates for risk-based pricing and open markets to ensure that financial protection remains both sustainable and affordable amidst a changing climate.

This paper summarizes the use of Extreme Value Theory (EVT) for modeling large insurance claims, particularly within reinsurance, where managing tail risk is paramount.
The core argument is that standard EVT must be adapted to overcome unique actuarial data challenges, including censoring (due to limits/delays), truncation (due to maximum possible losses), and data scarcity.
Key adaptations discussed include:
Truncation and Tempering Models to account for limits or weakening tail behavior.
Censoring-Adapted Estimators (e.g., modified Hill) for incomplete data.
Splicing/Composite Models that combine body and tail distributions (e.g., Mixed Erlang/Generalized Pareto) for a full-range fit.
Advanced Regression and Multivariate Models to incorporate covariates (like climate change effects) and analyze spatial dependencies.
A profound, tailored application of EVT is deemed critical for sound pricing and risk management of catastrophic risks.

This paper addresses the difficulty of 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, 𝗵𝗶𝗴𝗵-𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗱𝗮𝘁𝗮, 𝘀𝘂𝗰𝗵 𝗮𝘀 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗮𝗻𝗱 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗶𝗺𝗮𝗴𝗲𝗿𝘆, 𝗶𝗻𝘁𝗼 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲.
The study proposes a novel multi-view contrastive learning framework designed to generate low-dimensional spatial embeddings. This method aligns data from multiple sources (e.g., satellite imagery and OpenStreetMap features) with coordinate-based encodings.
The resulting embeddings are shown to consistently improve predictive accuracy in risk models, demonstrated through a case study on French real estate prices. The paper highlights that the embeddings capture spatial structure, enhance model interpretability, and exhibit transferability to unobserved regions.