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The concept proposes verifiable uncertainty akin to classical lotteries, suggesting it as a fundamental way to comprehend uncertainty. Rules are outlined for contrasting general events with verifiable lottery-like situations. Decision-making involves evaluating verifiable uncertainty differently from unverifiable uncertainty, allowing distinct attitudes and conservative handling of the latter. This approach forms a more solid theoretical basis for decision-making.

Privacy, a misunderstood concept in today's digital era, lacks a clear definition. People increasingly share personal data without fully understanding privacy policies. Studies show minimal engagement with these policies: only 9% always read them, while 36% never do. Mastery of impact and risk assessments is crucial for establishing robust privacy standards and maturity.

The AI Act, initially overlooking multifunctional AI like foundation models, led to debates. Industry sought exemption, civil groups pushed for inclusion, foreseeing safety gaps and burdens on users. "General Purpose AI systems" (GPAIS) emerged in discussions, aiming to extend Act requirements to adaptable models, addressing operator responsibility. Current debate focuses on adapting the Act to cover these advanced AI, revealing its initial limitations. The paper will delve into this evolution, highlighting challenges and proposing policy adjustments for GPAIS regulation within the AI Act's framework.

"Using a novel firm-level measure of cybersecurity, we find that cybersecurity risk increases the probability of bank default. The effect is larger for banks with deposit withdrawal, but less pronounced for banks with liquidity buffer. Our results are robust to using an instrumental variable approach and to using alternative measures. "

A new model for disability insurance tackles delays in claims by evolving in real-time. Unlike traditional methods, it adjusts reserves based on immediate information. By proposing modified reserves and estimators, it addresses delays effectively, demonstrated with real data, offering practical solutions for disability insurance schemes.

Bank regulators link capital to risk, but accurately measuring risk poses challenges. Banks use internal models, impacting Value-at-Risk (VaR) predictions and their exceedance frequency. Analyzing data, we find varied VaR and violations due to simulation methods, historical data, and holding periods. Banks’ modeling choices can reduce capital requirements strategically, potentially compromising the system's stability.

“While the main discussion of the paper is tailored to the management of systemic cyber risk in digital networks, we also draw parallels to similar risk management frameworks for other types of complex systems.”

The study explores the link between capital ratios and bank portfolio choices during financial strain using a unique approach. By treating portfolio adjustments as discrete decisions and comparing expected correlations to Bayesian model estimates, it identifies primary factors guiding banks' responses. Analyzing US commercial banks during the 1990s Credit Crunch, it suggests that while Basel Accord's risk-based capital requirements weren't the primary driver, banks likely responded to capital shocks, navigating constraints from leverage ratio requirements, and reacting to economic conditions.

The Three Lines of Defence model (based on defence-in-depth approaches) has become one of the primary risk management frameworks. Yet, its application in the cybersecurity space, one of the fastest-growing areas of risk for modern organisations, has been fragmented at best. In this article, we conducted a systematic literature review on the application of this model in cybersecurity.

A new copula class, Principal Component Copulas, merges copula-based methods with principal component models. It excels in modeling tail dependence in multivariate data by leveraging key directions. These copulas resemble factor copulas but exhibit distinct technicalities. They offer advantages in complex dependency modeling, especially in high dimensions, as demonstrated in simulations and applied to return data. Notably, they mitigate dimensionality issues in large models and excel in assessing tail risk, crucial for capital modeling.