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Cette étude de recherche de la Banque centrale des Pays-Bas propose un cadre d'analyse top-down pour évaluer l'impact des chocs environnementaux sur la stabilité financière. Les auteurs s'appuient sur le modèle de Merton pour traduire la dégradation de la nature, particulièrement le manque d'eau, en une hausse de la probabilité de défaut des entreprises. L'étude lie ainsi les composantes biophysiques, macroéconomiques et financières en quantifiant la vulnérabilité des actifs selon leur secteur et leur géographie. Les résultats démontrent qu'une perte de production de 10 % dans l'UE entraînerait une érosion significative des ratios de fonds propres des banques et de la solvabilité des assureurs. Enfin, le rapport explore l'extension de cette méthode aux risques croisés du climat et de la biodiversité pour une gestion prudencielle plus globale.

Un document d'analyse, publié conjointement par le Mécanisme européen de stabilité (MES) et l'Autorité européenne des assurances et des pensions professionnelles (EIOPA), propose une stratégie commune pour remédier au manque de couverture assurantielle face aux catastrophes naturelles. Les sources expliquent qu'un mécanisme de partage des risques à l'échelle européenne permettrait de réduire l'exposition financière des États et des citoyens grâce à une diversification géographique et sectorielle accrue. Le dispositif suggéré repose sur la création d'un pool d'assurance mutuel soutenu par un dispositif de soutien financier (backstop) sous forme de prêts publics à taux avantageux. Cette structure hybride vise à accroître la capacité de souscription du secteur privé tout en garantissant la stabilité financière face aux événements climatiques extrêmes. Enfin, l'étude démontre que cette approche pourrait réduire considérablement le besoin en capital des assureurs et favoriser des primes plus abordables pour les assurés.

Cet article examine les défaillances systémiques de la gestion des risques moderne, souvent trop dépendante de modèles mathématiques mal compris par les dirigeants. L'auteur propose de redéfinir le risque non plus comme une probabilité, mais comme une chaîne d'erreurs humaines et opérationnelles menaçant les objectifs stratégiques. En s'appuyant sur des crises historiques comme celle de Lehman Brothers ou la tragédie du submersible Titan, il souligne l'importance de surveiller les expositions massives aux actifs réels et financiers. La gestion active doit ainsi privilégier la correction des erreurs de jugement avant que les risques ne se manifestent concrètement. Enfin, l'ouvrage suggère cinq méthodes pratiques, telles que la diversification et le transfert de risque, pour protéger la pérennité des entreprises. Cette approche se veut plus accessible et efficace face à la volatilité croissante de l'économie mondiale.

This article argues that the United States experiences the highest per-capita flood losses among industrialized nations and attributes this to federal flood risk governance that has resisted adaptive reforms seen elsewhere. It presents a multi-criteria framework to assess governance quality and compares the National Flood Insurance Program with systems in other countries. Based on qualitative analysis of legal and policy documents, the study assigns a low adaptive governance score (1.9/5). It identifies key institutional barriers and highlights missing policy revision cycles and long-term planning. The paper proposes reform principles and situates findings within debates on climate resilience and governance.

This paper analyzes UK home insurance data (2009–2024) to examine how premiums and coverage adjust to flood risk. It reports that properties experiencing a nearby flood are significantly more likely to have flood coverage excluded the following year. The study finds that, before a government–industry risk-sharing scheme, higher-risk properties faced higher premiums and substantially greater exclusion rates than lower-risk ones. After the scheme’s introduction, premium differences decreased, but higher-risk properties continued to experience notably higher rates of flood coverage exclusion.

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.