The UK regulator plans to simplify its insurance rulebook by removing outdated and duplicate requirements, aiming to reduce costs and increase market access while maintaining customer protection.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.
Researchers proposed a new risk metric for evaluating security threats in Large Language Model (LLM) chatbots, considering system, user, and third-party risks. An empirical study using three chatbot models found that while prompt protection helps, it's not enough to prevent high-impact threats like misinformation and scams. Risk levels varied across industries and user age groups, highlighting the need for context-aware evaluation. The study contributes a structured risk assessment methodology to the field of AI security, offering a practical tool for improving LLM-powered chatbot safety and informing future research and regulatory frameworks.
The European Union’s AI Act significantly reshapes corporate governance, imposing new responsibilities on directors, compliance officers, in-house counsels, and corporate lawyers. It demands transparency, risk management, and regulatory oversight for AI systems, particularly high-risk ones. These professionals must integrate AI oversight into governance, manage liability, conduct impact assessments, and ensure cross-border compliance. With its extraterritorial reach, the Act influences non-EU entities and sets global standards for AI governance. This paper aims to offer strategic guidance on aligning corporate policies with these emerging legal requirements, emphasizing proactive risk management and ethical AI adoption.
“The European Insurance and Occupational Pensions Authority (EIOPA) has published the technical information on the symmetric adjustment of the equity capital charge for Solvency II with reference to the end of April 2025.”
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.
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.
As all transactions become digital, any involvement with EU users-even minor-triggers complex compliance risks, shifting the landscape from predictable “risk” to broader “uncertainty.” Compliance now dominates, reducing litigable individual rights and increasing disputes, but with a trend toward alternative and online dispute resolution (ADR/ODR). Traditional contract and litigation strategies are less effective, as mandatory compliance overrides forum or law choices. Future disputes will increasingly involve digital elements, requiring new approaches and cooperation between parties, especially regarding AI, data, and cybersecurity. Litigation will not decrease, but its nature will fundamentally change, demanding innovative risk management in international commercial litigation.
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.