Nos derniers articles

The paper promotes better #riskmanagement and the fair allocation of #liability in #ai-related accidents.

#bayesian data imputation is a technique used to fill in missing data in a variety of fields, including #riskmanagement. By employing imputation techniques to fill in the gaps, #riskmanagers can obtain a more comprehensive and reliable understanding of the underlying #risk factors, enabling them to make informed decisions and develop effective strategies for #riskmitigation.

The paper explores the potential inclusion of #climatechange #risks in the #prudential #regulatoryframework, specifically discussing adjustments to #capitalrequirements and changes to the #riskmanagement and #governance framework. The paper argues in favor of the latter but is more cautious regarding the former.

"... its ability to provide relevant #riskmitigation strategies was identified as its strongest aspect. However, the research also revealed that #chatgpt's consistency in #riskassessment and prioritization was the least effective aspect. This research serves as a foundation for future studies and developments in the field of #ai-driven #riskmanagement, advancing our theoretical understanding of the application of #aimodels like ChatGPT in #realworld #risk scenarios."

The proposed φ-Divergence Quadrangle presents a refined methodology for understanding and #managing #risk, contributing to the ongoing development of #riskassessment and #riskmanagement strategies.

"#expectile is a #risk measure that, similar to #var (quantile) and CVaR (superquantile), can be employed in #riskmanagement."

The paper discusses the importance of #riskinformed #decisionmaking -and the use of #riskassessment to support decisions. It highlights the need for a more dynamic approach to #riskmanagement, which takes into account #uncertainty, changes in systems, phenomena, or values that could alter the underlying premises of the initial risk assessment.

This study examines the #riskallocation problem in distributed #insurance using #blockchaintechnology, considering different charging methods. Through #gametheory analysis, the research explores the #pareto optimal risk allocation method. The findings reveal that when charges occur during insurance signing, risk is proportionally distributed based on policyholders' #riskaversion coefficient. However, if the platform provider charges a fee proportional to the premium or actual risk, policyholders bear increased risk from others while their own risk is reduced, leading to decreased overall utility. These conclusions provide valuable insights for #blockchain insurance companies regarding user #riskmanagement and allocation.

We provide a #cyberrisk definition and classification scheme for #riskmanagement purposes, to be used as a data collection template for #financialinstitutions.

This paper introduces the application of Deep Reinforcement Learning (#drl) in #alm, addressing limitations of traditional methods reliant on human judgement. The findings highlight the potential of DRL to enhance #riskmanagement outcomes for #insurers, #banks, #pensionfunds, and #assetmanagers, providing improved adaptability to changing market conditions.