758 résultats pour « Autre »

France Assureurs: Cartographie prospective 2025 des risques de la profession de l’assurance et de la réassurance

• 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.

Explainable AI: Can the AI Act and the GDPR go out for a Date?

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.

Insurance in a Changing Climate: A Retrospective Study of Water‑Related Claims and Pricing Strategies in Norway

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.

Operational Risk and Corporate Sustainability Relationship Using Case‑Based Reasoning

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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.

Generative AI and Its Role in Shaping the Future of Risk Management in the Banking Industry

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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.

A Random Forest approach to detect and identify Unlawful Insider Trading

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.

Some remarks on the effect of risk sharing and diversification for infinite mean risks

Insurance typically benefits risk-averse individuals by pooling finite-mean risks. However, with infinite-mean distributions (e.g., Pareto, Fréchet), risk sharing can backfire, creating a "nondiversification trap." This applies to highly skewed distributions like Cauchy or catastrophic risks with infinite losses. Open questions remain about these complex scenarios.

Differentiable Inductive Logic Programming for Fraud Detection

Explainable AI (XAI) is becoming increasingly important, especially in fields like fraud detection. Differentiable Inductive Logic Programming (DILP) is an XAI method that can be used for this purpose. While DILP has scalability issues, data curation can make it more applicable. While it might not outperform traditional methods in terms of processing speed, it can provide comparable results. DILP's potential lies in its ability to learn recursive rules, which can be beneficial in certain use cases.