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This document analyzes the impact of model uncertainty (ambiguity) on the insurance industry.
The study employed a 𝗿𝗼𝗯𝘂𝘀𝘁 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 that assumes insurers adopt strategies to maximize value against a "worst-case" scenario. The views expressed are that this leads to a new competitive market equilibrium characterized by:
• 𝗦𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁𝗹𝘆 𝗵𝗶𝗴𝗵𝗲𝗿 𝗽𝗿𝗲𝗺𝗶𝘂𝗺𝘀 and 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗲𝗾𝘂𝗶𝘁𝘆 𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀.
• 𝗠𝗼𝗿𝗲 𝗰𝗼𝗻𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝘃𝗲 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, evidenced by higher precautionary reserves and delayed dividend payouts.
• 𝗦𝘂𝗯𝘀𝘁𝗮𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗽𝗿𝗼𝗹𝗼𝗻𝗴𝗲𝗱 𝘂𝗻𝗱𝗲𝗿𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲, increasing in numerical simulations from 9.6 to 26 years.
• A long-run capacity distribution that is 𝗺𝗼𝗿𝗲 𝗰𝗼𝗻𝗰𝗲𝗻𝘁𝗿𝗮𝘁𝗲𝗱 𝗶𝗻 𝗹𝗼𝘄-𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝘀𝘁𝗮𝘁𝗲, implying slower recovery from adverse shocks.
The paper suggests these findings offer a theoretical explanation for the difficulty of detecting underwriting cycles in empirical data.

The report examines global insurability challenges and opportunities through the “4 A’s” framework—Awareness, Accessibility, Affordability, and Availability. It identifies major pressures from geopolitical instability, natural catastrophes, cyber risks, inflation, and technological shifts, contributing to a $1.83 trillion global protection gap. The report outlines barriers such as low financial literacy, limited market access, rising costs, and reduced coverage options in high-risk areas. It highlights industry initiatives and public-private collaborations addressing these issues and presents policy recommendations aimed at improving education, enabling innovation, reducing taxation, and fostering regulatory environments that sustain risk-based insurance markets.

This study on the insurance sector’s digital transformation highlights a paradox: adopting technologies like cloud computing, AI, and IoT enhances efficiency but increases cybersecurity risks. A survey of 150 professionals and interviews with 15 executives show a strong correlation (r = .78, p < .01) between digital technology use and security incidents, with phishing (88%), cloud misconfigurations (45%), and IoT vulnerabilities (25%) prevalent. Traditional defenses score high (e.g., network perimeter: 4.1/5), but IoT and software supply chain security lag (2.4–2.7/5). Cyber insurance now uses dynamic risk assessments, with 90% of underwriters employing external security ratings and 75% applying surcharges for high-risk technologies.

The provided text is an **academic article** that offers a comprehensive **analytical review of cyber risk management** within the insurance industry, focusing heavily on the **mathematical models** used for risk quantification and premium pricing. The review systematically covers the current state-of-the-art in cyber risk, discussing how dynamic and interconnected threats challenge traditional actuarial methods, necessitating the use of advanced quantitative tools like **stochastic models and copulas** to manage dependencies and calculate **Solvency Capital Requirements (SCR)**. It thoroughly details various **vulnerability functions** (including the well-known Gordon-Loeb model and its extensions) and different **premium calculation principles** (such as Expected Value and Mean-Variance), concluding that closer collaboration between different disciplines is essential for developing **robust cyber insurance and reinsurance solutions** in an increasingly digital landscape.

This research addresses the critical challenge of model ambiguity in insurance, where the true probabilities of losses are uncertain. It introduces randomly distorted Choquet integrals, a novel mathematical tool for creating flexible and dynamic risk measures. This provides a formal, unified methodology to resolve expert disagreements by extending industry-standard metrics like Value at Risk (VaR) and Average Value at Risk (AVaR). The framework allows a decision-maker to synthesize divergent opinions—whether on key parameters like a VaR confidence level or on the fundamental risk model itself (e.g., VaR vs. AVaR)—into a single, coherent, and scenario-dependent assessment.

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.