Nos derniers articles

This paper investigates dynamic insurance pricing and risk management when insurers face correlation ambiguity between underwriting and financial investment risks. By employing a robust control framework and G-expectation theory, the research models how insurers make decisions under worst-case beliefs regarding these unknown dependencies. The authors identify five distinct equilibrium regimes, such as pure underwriting or zero underwriting, which shift based on market conditions and ambiguity levels. A key finding challenges traditional assumptions by showing that uncertainty does not always lead to higher premiums or reduced utility for the insurer. Instead, ambiguity aversion can sometimes improve an insurer’s position by encouraging more conservative and robust portfolio allocations. Ultimately, the study highlights that accurately understanding risk dependence is essential for effective regulatory policy and equilibrium pricing in modern financial markets.

Fairness in machine learning is vital, especially as AI shapes decisions across sectors. In insurance pricing, fairness involves unique challenges due to regulatory demands for transparency and restrictions on using sensitive attributes like gender or race. Traditional fairness methods may not align with these specific requirements. To address this, the authors propose a tailored approach for building fair insurance models using only privatized sensitive data. Their method ensures statistical guarantees, operates without direct access to sensitive attributes, and adapts to varying transparency needs, balancing regulatory compliance with fairness in pricing.

The insurance sector faces pressure from rising catastrophic risks, leading to higher premiums and policy non-renewals. This paper proposes an arbitrage-free method for pricing catastrophe reinsurance using the compound dynamic contagion process and Esscher transform. The findings help insurers assess liabilities amid emerging risks like climate change, cyberattacks, and pandemics.

This paper develops a k-generation risk contagion model in a tree-shaped network for cyber insurance pricing. It accounts for contagion location and security level heterogeneity. Using Bayesian network principles, it derives mean and variance of aggregate losses, aiding accurate cyber insurance pricing. Key findings benefit risk managers and insurers.

This paper emphasizes the need for metrics to assess discriminatory effects and trade-offs. It introduces a sensitivity-based measure for proxy discrimination, defining admissible prices and using L2-distance for measurement, and proposes local measures for policyholder-specific analysis.

The paper explores the use of machine learning, particularly deep learning techniques, in insurance pricing by modeling claim frequency and severity data. It compares the performance of various models, including generalized linear models and neural networks, on insurance datasets with diverse input features. The authors use autoencoders to process categorical variables and create surrogate models for neural networks to translate insights into practical tariff tables.