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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 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.

FERMA supports the EIOPA and ECB's proposal for a European public-private reinsurance scheme to address the natural catastrophe protection gap. While backing the risk-based premium model and the potential for price stability, FERMA emphasizes the need for reliable and consistent data collection across nations. They also highlight the importance of a sufficiently large EU pool to manage premium pricing, a clear regulatory framework avoiding unnecessary burdens, and mechanisms to encourage long-term private sector engagement beyond annual renewals. FERMA advocates for continuous consultation and leveraging the scheme to incentivize risk prevention.

The EBA has launched an ESG dashboard to monitor climate risks across the EU/EEA banking sector using Pillar 3 disclosures. It benchmarks transition and physical risks, revealing high bank exposure (>70%) to carbon-intensive sectors, suggesting significant transition risk. Physical risk exposure is lower (<30%), but data granularity varies. Around half of real estate lending has relatively high energy efficiency, though data relies on estimates. The Green Asset Ratio (GAR) is low (~3%), reflecting the early stage of EU Taxonomy alignment. This framework supports the monitoring of climate-related financial stability risks. The dashboard uses data from December 2023 and June 2024.

L’ACPR et Tracfin actualisent leurs lignes directrices sur la vigilance et les déclarations liées à la lutte contre le blanchiment et le financement du terrorisme (BC-FT). Cette mise à jour intègre les évolutions législatives, les décisions récentes de la Commission des sanctions, les constats sur les dispositifs de surveillance automatisés, l’IA et les nouveaux risques. Elle précise les attentes envers les organismes financiers pour détecter, analyser les opérations atypiques et structurer les déclarations de soupçon afin d’en garantir la qualité. La dernière version datait de 2018.

The Format for Incident Reporting Exchange (FIRE) is a framework developed by the FSB to standardize the reporting of cyber and operational incidents across borders. FIRE, created with private sector collaboration, aims to promote consistency, improve communication, and address the challenges of reporting to multiple authorities. It offers standardized information items and flexibility for various operational and cyber incidents, and can be used by third-party service providers and organizations outside the financial sector. The FSB provides a downloadable taxonomy package to facilitate FIRE's global adoption and plans to review its implementation in 2027.

The EU prioritizes cybersecurity and data protection due to rising cyber threats and digital transformation. It employs regulations like GDPR for personal data and the NIS Directive for critical infrastructure resilience. This study analyzes their impact, challenges, and interplay, also comparing them globally to assess effectiveness in safeguarding digital security and fostering trust.

“This update is based on the EBA reporting framework version 4.0 and covers indicators on institutions' profitability, solvency and operational risk, among others. The update also includes a new sets of risk indicators laid down in the Banking Package (Capital Requirements Regulation and Capital Requirements Directive - CRR3/CRD6), indicators related to Environmental, Social and Governance (ESG), and those already used in the context of the Minimum Requirement for Own Funds and Eligible Liabilities (MREL).”

Fairness in machine learning is vital, especially as AI shapes decisions across sectors. In insurance pricing, fairness involves unique challenges due to regulatory demands for transparency and restrictions on using sensitive attributes like gender or race. Traditional fairness methods may not align with these specific requirements. To address this, the authors propose a tailored approach for building fair insurance models using only privatized sensitive data. Their method ensures statistical guarantees, operates without direct access to sensitive attributes, and adapts to varying transparency needs, balancing regulatory compliance with fairness in pricing.