Nos derniers articles

The paper provides critical theoretical and practical contributions to actuarial science by demonstrating the often-overlooked significance of higher-order mixed moments. It offers tools for robust risk assessment through sharp bounds and standardized rank coefficients. The findings emphasize that while higher-order moments often have a monotonic effect on overall capital requirements and life annuity pricing, their influence on individual risk contribution can be highly nuanced. This calls for actuaries and risk managers to move beyond traditional second-order moment analysis and carefully consider complex dependence structures to ensure accurate risk management and pricing in insurance.

This opinion and accompanying report from the 𝗘𝗕𝗔 provides a comprehensive overview of 𝗺𝗼𝗻𝗲𝘆 𝗹𝗮𝘂𝗻𝗱𝗲𝗿𝗶𝗻𝗴 (𝗠𝗟) 𝗮𝗻𝗱 𝘁𝗲𝗿𝗿𝗼𝗿𝗶𝘀𝘁 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗻𝗴 (𝗧𝗙) 𝗿𝗶𝘀𝗸𝘀 across the EU's financial sector from 2022 to 2024. The EBA, mandated to issue such an opinion biennially, identifies evolving threats driven by technological innovation, including vulnerabilities in FinTech, RegTech, and crypto assets, alongside the 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝘀𝗼𝗽𝗵𝗶𝘀𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗳𝗿𝗮𝘂𝗱 𝗮𝗻𝗱 𝗰𝘆𝗯𝗲𝗿𝗰𝗿𝗶𝗺𝗲 𝘀𝗰𝗵𝗲𝗺𝗲𝘀. While acknowledging positive developments like reduced tax crime risks and improved supervisory engagement in certain areas, the EBA highlights persistent challenges such as 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗮𝗻𝘁𝗶-𝗺𝗼𝗻𝗲𝘆 𝗹𝗮𝘂𝗻𝗱𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗰𝗼𝘂𝗻𝘁𝗲𝗿-𝘁𝗲𝗿𝗿𝗼𝗿𝗶𝘀𝘁 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗻𝗴 (𝗔𝗠𝗟/𝗖𝗙𝗧) 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗲𝗱 𝗽𝗿𝗼𝗺𝗶𝗻𝗲𝗻𝗰𝗲 𝗼𝗳 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗖𝗗𝗗) 𝘀𝗵𝗼𝗿𝘁𝗰𝗼𝗺𝗶𝗻𝗴𝘀. The report underscores the critical need for regulatory clarity and a more unified application of risk-based approaches throughout the EU's financial landscape.

The 𝗘𝗜𝗢𝗣𝗔 has evaluated 𝗵𝗼𝘄 𝗘𝘂𝗿𝗼𝗽𝗲𝗮𝗻 𝗶𝗻𝘀𝘂𝗿𝗲𝗿𝘀 𝗮𝗿𝗲 𝗶𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗶𝘀𝗸𝘀 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲𝗶𝗿 𝗿𝗶𝘀𝗸 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁𝘀, specifically within their 𝗢𝗥𝗦𝗔. The findings indicate that most insurers are now including both 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 𝗿𝗶𝘀𝗸𝘀 in their ORSA, utilizing 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 more frequently to understand potential financial impacts. While progress has been made, challenges remain, such as 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗿𝗲𝗴𝗶𝗼𝗻𝘀 and a 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲 𝗼𝗳 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗮𝘁𝗮. EIOPA aims to continue fostering 𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗼𝗿𝘆 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 and building capacity in this area.

This article claims that Generative AI (GenAI) is revolutionizing actuarial science, as demonstrated in four case studies. Large Language Models enhance claims cost prediction by extracting features from unstructured text, reducing errors. Retrieval-Augmented Generation automates market comparisons by processing document data. Fine-tuned, vision-enabled LLMs excel in classifying car damage and extracting contextual details. A multi-agent system autonomously analyzes datasets and generates detailed reports. GenAI also shows promise in automating claims processing, fraud detection, and document compliance verification. Challenges include regulatory compliance, ethical concerns, and technical limitations, emphasizing the need for careful integration of GenAI in insurance workflows.

This paper presents a unified framework for reinsurance markets with multiple insurers and reinsurers, using Choquet risk measures and nonlinear pricing. It identifies Subgame Perfect Nash Equilibrium as the optimal concept, proving contracts are rational and Pareto optimal, with insurer welfare gains over monopoly scenarios.

For years, "continuous monitoring" in cybersecurity lacked a clear definition, forcing improvised security practices. This paper introduces QUARC, a formal model that quantifies cybersecurity risk and links it to precise detection and response times. QUARC provides a robust, weight-free probabilistic risk function, translating this risk into concrete operational cadences using hazard and queue theories. This model offers a universal standard, allowing regulators to enforce testable compliance, security teams to monitor real-time conformance, and insurers to price risk accurately. QUARC transforms a vague policy into a measurable, enforceable reality, closing a critical loophole exploited by attackers.

This article presents modeling approaches—both structural and reduced-form—to improve the understanding and prediction of environmental risks. It enhances existing models for better risk assessment and pricing, particularly in infrastructure and land use contexts. Potential extensions include advanced temperature and rainfall modeling, such as stochastic mean-reversion and regime-switching Lévy processes. The paper also suggests future research comparing insurance pricing methods and exploring parametric insurance mechanisms, where payouts are triggered by measurable parameters rather than actual losses. These developments aim to refine environmental risk management and insurance strategies.

The UK regulator plans to simplify its insurance rulebook by removing outdated and duplicate requirements, aiming to reduce costs and increase market access while maintaining customer protection. Proposed changes include exempting large commercial clients from some conduct rules, reducing mandatory annual product reviews, allowing flexible lead insurer arrangements, broadening bespoke contract exclusions, and eliminating certain training requirements. These reforms aim to boost competitiveness while protecting smaller clients. The regulator seeks feedback on these proposals by July 2, 2025, as part of its ongoing effort to streamline regulations and support industry growth.

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.

This study addresses a novel risk-sharing problem where an agent maximizes expected wealth under ambiguity, penalized by a chi-squared model ambiguity. The framework generalizes monotone mean-variance preferences and accommodates multiple reference models for applications like climate risk. Explicit solutions are derived for the insurer’s optimal risk-sharing strategy, decision measure, and wealth process, which depends linearly on auxiliary processes linked to Radon-Nikodym derivatives. The model penalization parameter affects wealth variance, and the optimal strategy considers the counterparty’s model and premium. Future work could explore Lévy-Itô processes, alternative divergences, or a Stackelberg game framework.