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The objective of this paper is to compare the most common available Risk quantification models: Fault Tree Analysis, Failure Mode Effective Analysis, and FAIR (Factor Analysis of Information Risk) Model.
“The analysis reveals that boards are ineffective in cybersecurity risk oversight due to a lack of IT knowledge, and cybersecurity expertise is largely absent at the board level.”
Operational risk modeling requires flexible distributions for non-negative values, particularly those exhibiting heavy-tail behavior. Composite or spliced models, like composite Tukey-type distributions, are gaining attention for their ability to handle extreme and ordinary observations effectively. This paper explores the flexibility of such distributions, offering empirical validation with operational risk data.
The Basel II advanced measurement approach often yields counterintuitive operational risk capital due to extreme loss events. To address this, the semi-nonparametric (SNP) model by Chen and Randall (1997) can enrich parametric model distributions. SNP shows improvement over parametric models, providing more intuitive capital estimates consistent with extreme value theory.
The Probability of Insurance Recovery (PoIR) is a key parameter, yet its estimation lacks prior modeling efforts. This paper aims to introduce a methodology for assessing PoIR within firm risk frameworks.
"Entropic Value-at-Risk (EVaR) ... was previously calculated explicitly only for the normal distribution. We succeeded ... to calculate EVaR for Poisson, compound Poisson, Gamma, Laplace, exponential, chi-squared, inverse Gaussian distribution and normal inverse Gaussian distribution with the help of Lambert function that is a special function, generally speaking, with two branches.”
"... This review conducts a comprehensive literature review delving into recent ransomware attacks to analyze key aspects, including the targeted organizations, attack vectors, threat actors, propagation mechanisms, and the resulting business impact… this study provides valuable insights emphasizing the importance of proactive defenses to mitigate the risks posed by this growing threat."
A meta-analysis of contingent valuation studies on voluntary insurance for low-probability, high-impact risks finds demand lower than expected. Stated willingness to pay (WTP) averages 87% of expected losses. Factors like loss probability information positively affect WTP, while income and age negatively influence it. Cultural and methodological factors also impact WTP.
The report highlights Federated Learning's (FL) benefits in claims loss modeling by enabling collaboration across multiple insurance datasets without data sharing. FL addresses data privacy concerns, rarity of claim events, and lack of informative factors. It enhances forecasting effectiveness while preserving data privacy, applicable beyond insurance to fraud detection and catastrophe modeling, fostering future collaborations.
Monte Carlo studies are conducted to compare the proposed spectral risk measure estimator with the existing parametric and non parametric estimators for left truncated and right censored data. Based on our simulation study we estimate the exponential spectral risk measure for three data sets viz; Norwegian fire claims data set, Spain automobile insurance claims and French marine losses.