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This academic paper proposes these 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:

• The analysis provides a framework for introducing index insurance in competition with traditional products, emphasizing demand and solvency.

• Key drivers for index insurance demand are policyholder risk aversion, compensation speed advantage over traditional products, and its pricing (loading factor).

• The proposed hybrid product effectively balances the strengths of both insurance types by applying index insurance where it is “most suitable for policyholders,” accelerating compensation, and potentially reducing premiums.

• The methodology can help insurers identify specific loss types for which index compensation is preferred, optimizing portfolio structure and claims management.

• Future work will address modeling demand for index insurance in situations where traditional indemnity-based insurance is unavailable, requiring a “more nuanced approach to calibrate the utility function.”

𝗘𝗜𝗢𝗣𝗔 has issued new guidance on supervising 𝗺𝗮𝘀𝘀-𝗹𝗮𝗽𝘀𝗲 𝗿𝗲𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 and 𝗿𝗲𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 clauses. This guidance, provided in two annexes to its 2021 Opinion on risk-mitigation techniques, aims to standardize supervisory approaches across Europe.
The first annex focuses on 𝗺𝗮𝘀𝘀-𝗹𝗮𝗽𝘀𝗲 𝗿𝗲𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲, offering detailed guidance for supervisors on its prudential treatment. It emphasizes ensuring a common European approach, particularly in light of recent high lapse risks in various markets. The guidance helps supervisors evaluate how elements like the measurement period, exclusions, or termination clauses affect risk transfer effectiveness and the 𝗦𝗼𝗹𝘃𝗲𝗻𝗰𝘆 𝗖𝗮𝗽𝗶𝘁𝗮𝗹 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁 (𝗦𝗖𝗥). A 12-month measurement period is generally expected, aligning with the SCR time horizon.
The second annex addresses 𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗰𝗹𝗮𝘂𝘀𝗲𝘀 𝗶𝗻 𝗿𝗲𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 agreements that could undermine effective risk transfer. It highlights provisions that release the reinsurer from responsibility for legitimate losses during the treaty period and scrutinizes contracts where reinsurers can unconditionally retain transferred premiums and assets upon termination while being freed from obligations. These annexes promote supervisory convergence and fair competition within the market.

This study develops a machine learning framework to identify high-risk enterprise financial reports, comparing Support Vector Machine, Random Forest, and K-Nearest Neighbors models. Using 2020–2025 audit data from the Big Four firms, Random Forest showed the highest performance (F1-score: 0.9012), excelling in detecting fraud and compliance issues. While KNN struggled with high-dimensional data, SVM performed well but was computationally intensive. The study highlights the potential of machine learning in auditing but notes limitations, including reliance on structured data and exclusion of external economic factors.

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.

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.

All strategic and operational decisions should consider risk-adjusted earnings value, as all management inherently involves risk management. Effective risk management requires skilled personnel and a robust system to analyze, monitor, and manage risks, focusing on seven key areas: decision-oriented risk management, value-oriented corporate management, risk quantification (including economic, geopolitical, and sustainability risks), and risk aggregation using Monte Carlo simulations. A strong corporate strategy ensures financial sustainability and manageable earnings risks, while embedded risk management enables employees to address risks. These areas, underexplored in literature, warrant further attention, particularly risk aggregation through simulation methods.

The Cyber Due Diligence Object Model (CDDOM) is a structured, extensible framework designed for SMEs to manage cybersecurity due diligence in digital supply chains. Aligned with regulations like NIS2, DORA, CRA, and GDPR, CDDOM enables continuous, automated, and traceable due diligence. It integrates descriptive schemas, role-specific messaging, and decision support to facilitate supplier onboarding, risk reassessment, and regulatory compliance. Validated in real-world scenarios, CDDOM supports automation, transparency, and interoperability, translating compliance and trust signals into machine-readable formats. It fosters resilient, decision-oriented cyber governance, addressing modern cybersecurity challenges outlined in recent research.

This study extends the Gordon–Loeb model for cybersecurity investment by incorporating a Hawkes process to model temporally clustered cyberattacks, reflecting real-world attack bursts. Formulated as a stochastic optimal control problem, it maximizes net benefits through adaptive investment policies that respond to attack arrivals. Numerical results show these dynamic strategies outperform static and Poisson-based models, which overlook clustering, especially in high-risk scenarios. The framework aids risk managers in tailoring responsive cybersecurity strategies. Future work includes empirical calibration, risk-averse loss modeling, cyber-insurance integration, and multivariate Hawkes processes for diverse attack types.

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 study introduces a novel capital allocation mechanism for banks, using game theory to assign capital requirements while enforcing macro-prudential standards. Based on competition for lower requirements, the approach employs insensitive risk measures from Chen et al. (2013) and Kromer et al. (2016), typically yielding a unique Nash allocation rule, while sensitive measures from Feinstein et al. (2017) may need additional conditions for uniqueness. The Eisenberg-Noe (2001) clearing system is analyzed for systemic risk, with numerical Nash allocations demonstrated. The study claims that further investigation into properties like continuity, monotonicity, or convexity is needed, noting that not all can hold simultaneously due to firm interactions.