Nos derniers articles

"An adverse development cover (ADC) is a form of an excess of loss reinsurance contract that provides coverage for future loss payments relating to claims incurred prior to a specified date… A framework for assessing the value of an ADC from the perspective of the ceding insurer is developed. This value assists in making decisions regarding the acquisition of an ADC, comparing available options on offer and accounting for the ADC under the IFRS17 accounting standard."

Attack graphs visually map potential attack paths in systems, aiding systematic vulnerability exploration. Enhancing them with countermeasures and consequences streamlines risk assessment, accommodating system or environment changes. Demonstrated through a case study, this method integrates with existing standards, addressing evolving threats for better risk management in computer systems.

"Since the global financial crisis of 2007–9, legal risk has become increasingly important for the banking sector. In Poland, the growth in importance is predominantly associated with the so-called regulatory tsunami, which has seen a constantly changing legal framework for bank operations..."

We explore the challenge of measuring causal effects in empirical analyses, particularly in areas like asymmetric information and risk management. It emphasizes the importance of causal analysis in policy evaluation and discusses various frameworks such as instrumental variable, difference-in-differences, and generalized method of moments. The analysis addresses questions related to risk management's impact on firm value, moral hazard in insurance data, separating moral hazard from adverse selection, and the causal relationship between liquidity creation and reinsurance demand. The findings suggest that appropriate methodologies can enhance the value of risk management in firms despite residual information problems in various markets.

Accurate insurance claims forecasting is vital for financial planning and risk management. This study introduces innovative variables, such as weather conditions and car sales, and employs Machine Learning algorithms to predict average insurance claims per quarter. Key influential variables include new car sales and minimum temperature with specific lags. The findings aid insurers in enhancing claims forecasting by considering additional parameters like weather and sales data.

We study risk processes with level dependent premium rate. Assuming that the premium rate converges, as the risk reserve increases, to the critical value in the net-profit condition, we obtain upper and lower bounds for the ruin probability. In contrast to existing in the literature results, our approach is purely probabilistic and based on the analysis of Markov chains with asymptotically zero drift.

The study exploits Basel III's sequential adoption and ultimate parent rule, creating two bank groups under different regulations in the same country: early-adopting subsidiaries (treated banks) and domestic banks (untreated). Using a difference-in-difference approach, it empirically identifies Basel III effects by comparing risk and performance changes before and after the 2015 implementation in non-adopting countries.

#bayesian data imputation holds significant importance in a variety of fields including #riskmanagement. Incomplete or missing data can hinder a thorough analysis of risks, making accurate decision-making challenging. By employing imputation techniques to fill in the gaps, risk managers can obtain a more comprehensive and reliable understanding of the underlying risk factors. This, in turn, enables them to make informed decisions and develop effective strategies for #riskmitigation.

Proactive cyber-risk assessment is gaining importance due to its potential benefits in preventing cyber incidents across various sectors and addressing emerging vulnerabilities in cyber-physical systems. This study presents a robust statistical framework, using mid-quantile regression, to assess cyber vulnerabilities, rank them, and measure accuracy while dealing with partial knowledge. The model is tested with simulated and real data to support informed decision-making in operational scenarios.

“The #eu draft for an #euaiact is a legal medley. Under the banner of #risk-based #regulation, the AI Act combines two repertoires of EU law, namely #productsafety and #fundamentalrights protection. However, the proposed medley can fail if it does not account for the structural differences between the two legal repertoires…”