830 résultats
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This paper introduces "co-opetition" (combining competition and cooperation) to reinsurance risk-sharing. A two-layer game-theoretic framework models insurer-reinsurer contracting and price competition (Stackelberg-Nash), followed by collaborative risk-sharing. The model, using mean-variance preferences, yields explicit equilibrium results, demonstrating the feasibility of analyzing complex reinsurance market dynamics. Future research could explore different preferences, premium principles, and market structures.
This paper explores continuous-time mean-variance reinsurance with heterogeneous beliefs, a novel approach. It finds complex optimal contracts, beyond standard types, and proves their uniqueness. Specific forms emerge under different belief assumptions. Critically, it shows this model better reflects real-world insurer decisions than models ignoring belief differences.
• Le dérèglement climatique rejoint les cyberattaques sur la première marche du podium des risques ;
• Les risques politiques et sociaux sont en forte hausse ;
• L’intelligence artificielle générative suscite une méfiance nouvelle ;
• De manière générale, l’environnement est encore plus risqué en 2025 qu’il ne l’était en 2024 ;
• Les inégalités et tensions sociales inquiètent les assureurs pour la société française.
This paper examines the interplay of the AI Act and GDPR regarding explainable AI, focusing on individual safeguards. It outlines rules, compares explanations under both, and reviews EU frameworks. The paper argues that current laws are insufficient, necessitating broader, sector-specific regulations for explainable AI.
This study examines climate change's impact on water-related home insurance claims in Norway using a unique dataset. It develops a statistical model to address claim data challenges, reveals geographical and seasonal risk patterns, and evaluates pricing strategies. The findings provide insights for insurers to adapt to evolving climate risks.
“As the latest climate-related crisis unfolds in Los Angeles, Treasury releases most comprehensive data on homeowners insurance in history, along with report detailing higher costs to homeowners and insurers of elevated climate perils.”
This research develops a taxonomy of operational risks impacting corporate sustainability. A literature review and analysis of 100 business cases reveal relationships between these risks, their causes, and their economic, social, and environmental consequences. The findings help companies classify and manage sustainability-related operational risks, though the specific relationships may vary across sectors and individual cases.
This lecture explores how probability theory can quantify uncertainty, chance, and even ignorance. He demonstrates methods to measure the quality of these quantified uncertainties. He also humorously admits a miscalculation during the lecture regarding paired comparisons within the audience.
Generative AI (GAI) is transforming banking risk management, improving fraud detection by 37%, credit risk accuracy by 28%, and regulatory compliance efficiency by 42%. GAI enhances stress testing but faces challenges in privacy, explainability, and skills gaps. Its adoption, led by larger banks, demands holistic strategies for equitable industry impact.
This study proposes a new method for detecting insider trading. The method combines principal component analysis (PCA) with random forest (RF) algorithms. The results show that this method is highly accurate, achieving 96.43% accuracy in classifying transactions as lawful or unlawful. The method also identifies important features, such as ownership and governance, that contribute to insider trading. This approach can help regulators identify and prevent insider trading more effectively.