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Lack of high-quality public cyber incident data hinders empirical research and predictive modeling for cyber risk. Companies' reluctance to disclose incidents, fearing reputational damage, perpetuates this challenge. Actuarial solutions focus on enhancing existing datasets and employing advanced modeling. A new InsurTech framework is proposed to enrich cyber incident data with entity-specific attributes, addressing the gap in publicly available information. Machine learning models predict incident types and estimate frequencies, demonstrating improved robustness when incorporating InsurTech-derived features. This framework aims to generate transparent, entity-specific cyber risk profiles, supporting tailored underwriting and proactive risk mitigation for insurers and organizations.

The paper 𝙏𝙝𝙚 𝙍𝙚𝙜𝙪𝙡𝙖𝙩𝙞𝙤𝙣 𝙤𝙛 𝘿𝙖𝙩𝙖 𝙋𝙧𝙞𝙫𝙖𝙘𝙮 𝙖𝙣𝙙 𝘾𝙮𝙗𝙚𝙧𝙨𝙚𝙘𝙪𝙧𝙞𝙩𝙮 by Jasmin Gider (Tilburg University - Tilburg University School of Economics and Management), Luc Renneboog (Tilburg University - Department of Finance), and Tal Strauss (European Central Bank ECB) compares and contrasts the regulatory landscapes of data privacy and cybersecurity in the EU and the US. It outlines the fragmented nature of US regulations, often relying on state-specific laws and sectoral approaches, in contrast to the EU's more unified framework like 𝗚𝗗𝗣𝗥 and 𝗡𝗜𝗦 Directives. The text details the increasing costs and frequency of cyber incidents, emphasizing the insufficient mandatory disclosure requirements in both regions. Furthermore, it identifies gaps in current legislation and ongoing efforts, such as the 𝗘𝗨'𝘀 𝗖𝘆𝗯𝗲𝗿 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗔𝗰𝘁 and the US.'s 𝗖𝗜𝗥𝗖𝗜𝗔, to enhance 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 and address underinvestment in 𝗰𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆.

The AMRAE study describes 2024 as a positive year for the cyber insurance market, with rising but manageable claim numbers. There's a notable increase in cyber insurance uptake, especially among intermediate and medium-sized businesses, suggesting broader market penetration.
For the first time in five years, premium volume slightly dropped, with an average 18% reduction in annual premium rates for large companies and declining deductibles, indicating increased market flexibility.
However, the report identifies emerging concerns. Claims and payouts for large companies are increasing significantly. Also, a slight capacity increase is not commensurate with rate decreases, suggesting large companies may have reduced budgets more than they've expanded capacity. The study emphasizes the continued importance of accurate cyber risk exposure measurement given geopolitical tensions and new attack vectors.

Financial institutions are increasingly dependent on third-party service providers (TPSPs), raising concerns about systemic risks due to limited transparency. While the EU and U.K. have introduced formal oversight regimes, the U.S. relies on industry cooperation and micro-prudential supervision. A recent case study highlights financial stability risks from a payments disruption linked to a TPSP. As rapid technological change reshapes the financial sector, vulnerabilities from TPSP concentration and interconnectedness may grow. Greater understanding is needed to assess these risks and inform potential oversight responses.

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 study explores how Machiavellianism, a manipulative personality trait, fuels malicious insider behavior through the Fraud Triangle’s elements: pressure, opportunity, and rationalization. Analyzing 768 U.S. employees via PLS-SEM, researchers found Machiavellianism strongly influences all three, with rationalization as the primary driver of unethical intent. The findings highlight rationalization’s role in justifying malicious acts, urging organizations to bolster ethical cultures and accountability to curb insider threats. By linking personality traits to situational factors, the study enhances cybersecurity risk modeling and advocates for behaviorally informed insider threat prevention strategies.

This paper introduces a robust method for evaluating Conditional Value-at-Risk (CVaR) when data distribution can't be simulated. Using rolling data windows as proxies for independent samples, the approach effectively assesses worst-case risk. Applied to Danish fire insurance data, it outperformed traditional DRO (distributional risk optimization) methods—achieving accurate, less conservative estimates in 87% of cases. This advancement enables reliable risk management even with limited tail data. Future research will focus on refining robustness guarantees and integrating extreme value theory into decision-making models involving rare but impactful events.

The report underscores the robustness of Europe’s insurance, reinsurance, and pension sectors despite a volatile macroeconomic environment. Strong capital positions persist, with median Solvency II ratios slightly down but stable. Premium growth surged, with non-life up 8.2% and life at 13.8%. Profitability improved, with median return on assets at 0.7%. However, it points out that risks from exchange rate volatility, elevated interest rates, geopolitical tensions, and cyber threats require vigilant monitoring. It also notes significant US equity exposure, urging caution amid potential market corrections.

Face à un contexte géopolitique tendu, France Assureurs appelle à réorienter le règlement FIDA pour un partage des données financières et d’assurance plus compétitif, sécurisé et centré sur le client. Trois priorités sont mises en avant : garantir la compétitivité via un déploiement progressif et une sécurité juridique accrue, préserver la souveraineté européenne en excluant les géants non-européens, et répondre aux besoins réels des clients avec un encadrement strict du traitement des données. Malgré des avancées dans les discussions, des ajustements restent nécessaires pour protéger les consommateurs et renforcer la cyber-résilience.

The German and European banking sector is undergoing rapid transformation due to digitalization, ESG integration, regulatory changes, demographic shifts, and increased competition from FinTechs. Key challenges include managing complexity, leveraging AI and data, optimizing business models, and ensuring resilience and security. Banks must adapt quickly to survive, with successful integration of AI and ESG being crucial. Consolidation and evolution towards technology-driven or platform-based approaches are likely. Banks face a "transformation trilemma" of managing digital, regulatory, and ESG changes while maintaining profitability.
THE PAPER IS IN GERMAN