Nos derniers articles

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.

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 paper extends prior work to model an insurance company facing a future "tipping point" where catastrophe risks increase. Using viscosity solutions of a Hamilton-Jacobi-Bellman equation, the authors solve an optimal control problem to find the best dividend strategy. They show that, under fair premium adjustments and full observability, increased catastrophe risk may benefit shareholders. Numerical examples support these findings, and future research may explore relaxing model assumptions.