This research examines how ESG performance impacts default probability (PD) in life and non-life insurance firms. Findings show that improved ESG practices reduce both short-term and long-term PD, benefiting credit ratings and financial stability. Policymakers and managers can use this to enhance risk management and sustainable finance strategies.
The study assesses the impact of Europe's Single Supervisory Mechanism on banks' balance sheets, finding that centrally supervised banks have higher Tier 1 capital ratios. This is influenced by capital requirements, business models, and credit risk, particularly in countries with less stringent regulations, leading to increased resilience.
This study assesses flood-related financial stability risks in the Netherlands through diverse scenarios for bank stress tests. Results show varying impacts on bank capital, amplified by climate change. Stronger defenses can mitigate some effects, and there's a non-linear relationship between flood damages and capital depletion, emphasizing extreme scenario consideration.
This research uses Monte Carlo simulations to examine managing basis risk in parametric insurance through diversification. Findings show that increasing contracts reduces risk and volatility, spatial relationships significantly affect risk levels, and disaster severity has little impact, suggesting severe events shouldn't limit parametric insurance development.
This article reviews the EU's Artificial Intelligence Act, highlighting its structure, scope, and key principles like fairness and transparency. It critiques the complexity of regulating high-risk AI, forbidden practices, and the risk of hindering responsible innovation despite an overall balanced framework.
This paper addresses actuarial challenges in insurance by developing a user-friendly algorithm for optimal reinsurance decisions, balancing capital efficiency and asset/liability management. It combines expert judgment with quantitative methods, overcoming computational barriers for non-specialists. The techniques can be applied to broader risk management problems in insurance.
This paper introduces a novel multivariate dependence model to better represent cyber breach risks by capturing temporal and cross-group dependencies. Using a semi-parametric and copula approach, it improves predictive performance and generates more profitable insurance contracts, outperforming existing models in managing cyber risk and insurance pricing.
The study explores an insurance company managing financial risk through reinsurance, aiming to optimize terminal wealth and minimize ruin probability. Using neural networks, it finds the optimal reinsurance strategy based on expected utility and a modified Gerber-Shiu function, illustrated by a numerical example involving a Cramér-Lundberg surplus model.
This study analyzes insurance claim processing delays due to limited capacity and backlogs. It proposes optimal capacity selection to minimize costs by accounting for delay-adjusted and fixed settlement costs, supported by theoretical insights and a large-scale numerical study to demonstrate practical application.
The lack of risk transfer stems from structural forces that deter innovation in insurance policies, leading to inefficient risk management and hindering market development. Policy responses can help address these issues.