This study examines how organizations conceptualize and manage cyber risk, finding a gap between the normative risk-based management approach and actual practices. Organizations often use qualitative assessments masked as quantitative, creating an illusion of precision. The study proposes "qualculation" as the highest standard for aligning cyber risk measurement and management.
Elicitable functionals and consistent scoring functions aid in optimal forecasting but assume correct distributions, which is unrealistic. To address this, robust elicitable functionals account for small misspecifications using Kullback-Leibler divergence. These robust functionals maintain statistical properties and are applied in reinsurance and robust regression settings.
Climate change poses financial risks to institutions. Quantifying these risks is difficult, limiting mitigation efforts. Scenario analysis helps assess risks by extending macro-climate scenarios to asset-level analysis. Despite advancements, limitations remain in applying this approach to financial modeling.
This study develops a new method to estimate U.S. homeowners insurance coverage and premiums, revealing significant under-insurance, especially among vulnerable borrowers in high-risk areas. It highlights the role of rising premiums and behavioral inertia in under-insurance, with potential impacts on mortgage and real estate markets.
Social media accelerates information spread, but also enables misinformation, risking bank runs and failures. This article examines the dangers of false information in banking, compares regulations in securities markets, and proposes regulatory and legislative solutions to protect insured depository institutions, evaluating their feasibility.
Organizations rely on complex supply chains, which, while efficient, introduce vulnerabilities. To ensure continuity and align with business strategy, companies must develop robust Supply Risk Management Capability Processes. This process enables proactive identification, assessment, and mitigation of potential disruptions, protecting operational continuity and financial performance. The article offers a detailed guide.
The RNN-HAR model, integrating Recurrent Neural Networks with the heterogeneous autoregressive (HAR) model, is proposed for Value at Risk (VaR) forecasting. It effectively captures long memory and non-linear dynamics. Empirical analysis from 2000 to 2022 shows RNN-HAR outperforms traditional HAR models in one-step-ahead VaR forecasting across 31 market indices.
This paper develops a k-generation risk contagion model in a tree-shaped network for cyber insurance pricing. It accounts for contagion location and security level heterogeneity. Using Bayesian network principles, it derives mean and variance of aggregate losses, aiding accurate cyber insurance pricing. Key findings benefit risk managers and insurers.
This paper examines the rise of algorithmic harms from AI, such as privacy erosion and inequality, exacerbated by accountability gaps and algorithmic opacity. It critiques existing legal frameworks in the US, EU, and Japan as insufficient, and proposes refined impact assessments, individual rights, and disclosure duties to enhance AI governance and mitigate harms.
The paper analyzes the EU's Artificial Intelligence Act and its impact on AI regulation in banking and finance. It highlights the Act's potential to enhance governance, address high-risk applications, and the need for better coordination among regulators. Findings suggest challenges remain, including the necessity for adaptive frameworks to ensure ethical AI deployment.