The study examines the relationship between ESG variables and financial risk, measured through logarithmic volatility. It introduces the Hierarchical Variable Selection (HVS) algorithm, designed for ESG datasets, which is reported to outperform aggregated ESG scores and traditional selection models by providing higher explanatory power with fewer variables. Findings suggest that ESG risk factors vary across sectors and between large- and small-cap firms, influenced by differences in regulation, expectations, and strategy. The authors highlight the robustness and adaptability of HVS, noting its effectiveness in identifying risk-relevant ESG variables across industries and its potential for broader applications in hierarchical datasets.
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.
The paper investigates whether Global Systemically Important Banks (G-SIBs) engage in stronger window-dressing practices than other banks. It finds evidence that G-SIBs reduce exposures such as assets, debt, and derivatives more sharply at year-end and then increase them again in the following quarter, creating a “V-shape” pattern. This behavior is more pronounced for G-SIBs near regulatory thresholds or with higher surcharges, suggesting attempts to lower capital requirements. The study highlights potential market implications and questions the effectiveness of the G-SIB framework, suggesting reforms such as using average exposures rather than year-end figures.
These responses from Insurance Europe to various consultations by EIOPA concerning the Insurance Recovery and Resolution Directive (IRRD) outline the insurance industry's feedback on guidelines for identifying critical functions, removing impediments to resolvability, criteria for pre-emptive recovery planning and market share determination, and the content of both recovery and resolution plans, as well as resolvability assessments. A recurring theme across these responses is the industry's call for proportionality, flexibility, and reduced administrative burden, emphasizing that the IRRD's application should consider the unique characteristics of the insurance sector, distinguishing it from banking. The responses also frequently highlight concerns about duplication with existing DORA and Solvency II requirements and the lack of quantitative cost assessments for proposed regulations.
This Financial Stability Paper proposes enhancements to its analytical framework, focusing on three key areas: 𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀, 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗿𝗶𝘀𝗸 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁, and 𝗲𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀. The paper suggests a more quantitative approach to the Financial Policy Committee's goals, a systematic way to identify and model financial shocks, and the development of a 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 to improve policy evaluation. This framework is intended to prepare for future challenges and serve as a basis for further discussion.
This article argues that there is an increasing erosion of the traditional public-private divide, which is a key principle of liberalism and the rule of law. The authors identify a gradual shift, starting with the "responsibilization" of private actors and progressing to risk-based regulation like the GDPR. They contend that the DSA and AI Act represent a new milestone, as they delegate regulatory powers to private companies, effectively turning them into regulators of their TPSPs. This “privatization of public action” is seen as a serious threat to the rule of law because it removes public action from public scrutiny. To address this, the authors suggest connecting the rule of law more closely with democracy, which could help set boundaries for the legislative conferral of regulatory powers to private entities.
This study presents the Bayesian Component Encoder Analysis (BCEA) method for identifying financial crime. Integrating FinBERT, principal component analysis (PCA), and Bayesian networks, BCEA uses financial texts, images, and transaction records. FinBERT extracts semantic features, PCA reduces data complexity, and Bayesian networks model features for probabilistic reasoning. The authors claim BCEA achieves 94.35% accuracy and a 12.78-second recognition time, surpassing LSTM and BERT models. The authors state that the method demonstrates potential for financial supervision and risk management, with possible applications in complex financial scenarios, based on experimental results validating its effectiveness.
AI is not just an incremental improvement but a "paradigm shift" in regulatory compliance. By automating KYC, AML, and transaction monitoring, financial institutions can achieve unprecedented levels of efficiency, accuracy, and risk management. However, this transformative potential comes with significant responsibilities regarding data governance, ethical considerations, and maintaining human oversight. Success in this evolving landscape will hinge on strategic AI implementation, continuous adaptation to regulatory changes, and strong collaboration across the industry and with regulatory bodies. The long-term goal is a more "secure and resilient financial ecosystem."
On 12 August 2025, the European Banking Authority (EBA) published a report on the use of supervisory technology (SupTech) in anti-money laundering and counter-terrorist financing (AML/CFT) oversight. It draws on a November 2024 survey of 31 competent authorities across 25 EU member states (plus three outside) and a January 2025 workshop with the European Commission’s AMLA Task Force.
Global Regulation Tomorrow
. The report notes that 47 % of SupTech tools are already in production, 38 % are under development, and 15 % are exploratory. Benefits include improved data quality, analytics, efficiency and collaboration, while challenges involve limited resources, governance issues, legal uncertainties and organizational readiness.
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This paper 𝗲𝘅𝗮𝗺𝗶𝗻𝗲𝘀 𝘁𝗵𝗲 𝗲𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗿𝗲𝗮𝘁 𝗼𝗳 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗳𝗿𝗮𝘂𝗱 𝗮𝗻𝗱 𝗰𝘆𝗯𝗲𝗿𝗰𝗿𝗶𝗺𝗲, highlighting how criminal organizations are rapidly adopting advanced AI, particularly generative AI, to execute sophisticated attacks. It details how these malicious uses lead to 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗹𝗼𝘀𝘀𝗲𝘀, 𝗺𝗼𝗿𝗲 𝗶𝗻𝘁𝗿𝗶𝗰𝗮𝘁𝗲 𝗰𝗿𝗶𝗺𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗮𝗻𝗱 𝗻𝗼𝘃𝗲𝗹 𝘀𝗰𝗮𝗺 𝘁𝘆𝗽𝗼𝗹𝗼𝗴𝗶𝗲𝘀, such as deepfakes and advanced phishing. The document also 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝘀 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗳𝗮𝗰𝗲𝗱 𝗯𝘆 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗱𝗲𝗳𝗲𝗻𝗱𝗶𝗻𝗴 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝘁𝗵𝗲𝘀𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝘀, citing issues like slow AI adoption, outdated risk management frameworks, and underinvestment in defense systems. Ultimately, it 𝗮𝗱𝘃𝗼𝗰𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘂𝗿𝗴𝗲𝗻𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝗮𝗴𝗶𝗹𝗲, 𝗔𝗜-𝘃𝗲𝗿𝘀𝘂𝘀-𝗔𝗜 𝗱𝗲𝗳𝗲𝗻𝘀𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 and emphasizes the critical need for industry-wide cooperation to counteract the evolving landscape of AI-enabled financial crime.