Nos derniers articles

Advanced #machinelearning models were found to be more effective than #linearregression in predicting firm performance under #naturaldisaster #risks. The study suggests that textual data in #financialreports can be used to measure the perceived natural disaster risk and predict its effects on firm performance.

The article delves into #ethical concerns with #aitools in #legal and #tax research, addressing #output #quality, #bias, #verifiability, #liability, and #privacy #risks. It explores #regulatory, #tech, and professional solutions, offering practical advice for tax professionals to safely navigate AI's challenges with #riskmitigation.

The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$-divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$-divergence-derived sets yield strong time-consistency, while Wasserstein distance leads to a new non-normalized time-consistency. Recursive representations of one-step conditional robust risk measures underlie strong or non-normalized time-consistency.

This study employs an agent-based #model to explore how #climate shocks spread within #supplychains, linking #climateimpacts to firms' #default #risks. Integrating supply chain and financial models, it outlines a framework to simulate physical risk transmission, downstream effects, and increased default risk. Findings underscore supply chains' role in #climaterisk propagation, advocate adaptation measures, and identify vulnerable sectors. The research underscores the necessity of climate #resilience in supply chains.

Companies use #ai tools for #hr decisions, but they face a balance between benefits and #risks. With limited federal #regulation and complex state laws, employers seek guidance. The #model#riskmanagement#mrm framework, adapted from #finance, aids in managing #airisks for #employment choices. Proportionality lets employers adjust validation to risks and tech changes. Objective analysis and a competent MRM team ensure AI tools align with design and legal requirements, fostering trust and #compliance.

In March 2023, rapid #bankruns led to the failures of #siliconvalleybank, Signature Bank, and First Republic Bank. Uninsured depositors lost confidence due to higher interest rates and their investment model. Other banks are also experiencing deposit outflows. A book by #nyustern faculty and others analyzes the situation, offering a diagnosis and policy proposals for #financialresilience, emphasizing adaptable and robust #banking policies amidst changing #risks.

#insurers have discretion to determine #solvencyii #capitalrequirements. We find that long-term guarantees measures substantially influence the reported solvency ratios. The measures are chosen particularly by less solvent insurers and firms with high interest rate and credit spread sensitivities. Internal #models are used more frequently by large insurers and especially for #risks for which the firms have already found adequate immunization strategies.

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.

The paper explores the challenges of building a #safetyculture for #ai, including the lack of consensus on #risk prioritization, a lack of standardized #safety practices, and the difficulty of #culturalchange. The authors suggest a comprehensive strategy that includes identifying and addressing #risks, using #redteams, and prioritizing safety over profitability.

"We use the recently failed #svb as a case study. Our [#machinelearning #textanalysis] findings indicate a weaker emphasis on #riskgovernance by SVB and an environment, particularly after 2011, where the #ceo became more dominant in influencing SVB’s #riskculture. We also show that despite recognition of the portfolio problems, SVB’s CEO’s tone indicated that #regulatorycompliance and #riskstrategy of the #bank would #mitigate these #risks. We observe an alignment between the #riskculture of SVB and other banks with the highest uninsured deposits as well as with two #us #gsibs."