Nos derniers articles

As extreme weather events intensify, insurers face limits in absorbing losses, necessitating a shift from post-event compensation to loss prevention. This requires interlinked public, public-private, and private solutions, with tough policy decisions on responsibilities and cost allocation. Insurers can leverage risk expertise, data, and technology to promote loss prevention through knowledge-sharing and financing household measures, fostering a cycle of enhanced insurability, reduced protection gaps, and business growth. While insurance law traditionally supports compensation, tailored loss prevention clauses could become standard, addressing protection gaps and creating transformative opportunities. Prevention surpasses post-event claims and uninsured losses.

All strategic and operational decisions should consider risk-adjusted earnings value, as all management inherently involves risk management. Effective risk management requires skilled personnel and a robust system to analyze, monitor, and manage risks, focusing on seven key areas: decision-oriented risk management, value-oriented corporate management, risk quantification (including economic, geopolitical, and sustainability risks), and risk aggregation using Monte Carlo simulations. A strong corporate strategy ensures financial sustainability and manageable earnings risks, while embedded risk management enables employees to address risks. These areas, underexplored in literature, warrant further attention, particularly risk aggregation through simulation methods.

The Cyber Due Diligence Object Model (CDDOM) is a structured, extensible framework designed for SMEs to manage cybersecurity due diligence in digital supply chains. Aligned with regulations like NIS2, DORA, CRA, and GDPR, CDDOM enables continuous, automated, and traceable due diligence. It integrates descriptive schemas, role-specific messaging, and decision support to facilitate supplier onboarding, risk reassessment, and regulatory compliance. Validated in real-world scenarios, CDDOM supports automation, transparency, and interoperability, translating compliance and trust signals into machine-readable formats. It fosters resilient, decision-oriented cyber governance, addressing modern cybersecurity challenges outlined in recent research.

This study extends the Gordon–Loeb model for cybersecurity investment by incorporating a Hawkes process to model temporally clustered cyberattacks, reflecting real-world attack bursts. Formulated as a stochastic optimal control problem, it maximizes net benefits through adaptive investment policies that respond to attack arrivals. Numerical results show these dynamic strategies outperform static and Poisson-based models, which overlook clustering, especially in high-risk scenarios. The framework aids risk managers in tailoring responsive cybersecurity strategies. Future work includes empirical calibration, risk-averse loss modeling, cyber-insurance integration, and multivariate Hawkes processes for diverse attack types.

EIOPA's April 2025 Insurance Risk Dashboard indicates stable, medium-level risks in the European insurance sector, though pockets of vulnerability exist due to geopolitical uncertainty and market volatility. Macroeconomic risks are stable but with concerning GDP growth and inflation forecasts. Credit risks remained stable until early April, when spreads widened slightly. Market risks are elevated due to bond and equity volatility. Liquidity, solvency, profitability, financial interlinkages, and insurance risks are stable. Market sentiment is medium risk, and ESG risks are steady but with an intensifying outlook due to shifting environmental agreements.

This study introduces a novel capital allocation mechanism for banks, using game theory to assign capital requirements while enforcing macro-prudential standards. Based on competition for lower requirements, the approach employs insensitive risk measures from Chen et al. (2013) and Kromer et al. (2016), typically yielding a unique Nash allocation rule, while sensitive measures from Feinstein et al. (2017) may need additional conditions for uniqueness. The Eisenberg-Noe (2001) clearing system is analyzed for systemic risk, with numerical Nash allocations demonstrated. The study claims that further investigation into properties like continuity, monotonicity, or convexity is needed, noting that not all can hold simultaneously due to firm interactions.

“This update is based on the EBA reporting framework version 4.0 and covers indicators on institutions' profitability, solvency and operational risk, among others. The update also includes a new sets of risk indicators laid down in the Banking Package (Capital Requirements Regulation and Capital Requirements Directive - CRR3/CRD6), indicators related to Environmental, Social and Governance (ESG), and those already used in the context of the Minimum Requirement for Own Funds and Eligible Liabilities (MREL).”

An agent with multiple loss models optimizes risk sharing with a counterparty using a mean-variance criterion adapted for ambiguity. Under a Cramér-Lundberg loss model, the optimal risk sharing contract and wealth process are characterized. The strategy is proven admissible, and the value function verified. The optimal strategy is applied to Spanish auto insurance data with differing models from cross-validation for numerical illustrations.

En 2024, la France vit plus que jamais dans une « société du risque» face aux tensions géopolitiques, au décrochage économique européen et à l'aggravation des risques climatiques (année la plus chaude, événements naturels coûteux). Les Français se sentent vulnérables et inquiets face aux risques de guerre et à la capacité future d'assurer les risques climatiques et autres. Le secteur de l'assurance, bien que créateur d'emplois et gérant un grand nombre de sinistres (dont le coût des événements naturels a atteint 5 milliards d'euros en France), fait face à une hausse de la sinistralité (dégâts des eaux, sinistres graves pour les professionnels, cyberattaques, sinistralité agricole record) et des coûts (réparation automobile, dépenses de santé).

The banking industry faces complex financial risks, including credit, market, and operational risks, requiring a clear understanding of the aggregate cost of risk. Advanced AI models complicate transparency, increasing the need for explainable AI (XAI). Understanding risk mathematics enhances predictability, financial management, and regulatory compliance in an evolving landscape.