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Insurers face complex risk dependencies in loss reserving. Additive background risk models (ABRMs) offer interpretable structures but can be restrictive. Estimation challenges arise in models without closed-form likelihoods. Using a modified continuous generalized method of moments (CGMM), comparable to Maximum Likelihood Estimation (MLE), addresses these challenges in certain loss reserving models, including stable distributions.

“Our findings underscore the critical role of comparability in enhancing financial decision-making, boosting investment efficiency, and mitigating ESG-related risks, offering valuable insights for corporate governance and strategic decision-making.”

“We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk.”

This study proposes an attention-based ensemble model for detecting credit card fraud, integrating classifiers' predictions using two aggregation operators (DOWA and IOWA). The model, which selects key features via a bootstrap forest, achieves 99.95% accuracy and a perfect AUC of 1, demonstrating the effectiveness of AI in fraud detection.

The research shows that AI biases often stem from organizational pressures like cost, risk, competition, and compliance, influencing development before technical factors are considered. These biases reflect broader societal and commercial contexts, with ethical considerations often sidelined. Recommendations focus on assessing technology's impact and organizational influences on AI biases.

“… this research provides valuable insights into the complexity of detecting and preventing fraudulent activities in crowdfunding and highlights effective detection techniques that, if implemented, offer promising solutions to enhance platform reputation and ensure regulatory compliance.”

"We mathematically demonstrate how and what it means for two collective pension funds to mutually insure one another against systematic longevity risk. The key equation that facilitates the exchange of insurance is a market clearing condition. This enables an insurance market to be established even if the two funds face the same mortality risk, so long as they have different risk preferences. Provided the preferences of the two funds are not too dissimilar, insurance provides little benefit, implying the base scheme is effectively optimal. When preferences vary significantly, insurance can be beneficial."

This paper explores moral hazard in insurance when individuals test for risk severity. It highlights how regulations and loss reduction costs impact behavior. Monetary costs lead to uniform loss reduction, while convex costs drive higher-risk individuals to reduce losses more. Insurers can incentivize risk discovery and reduction through tailored contracts.

This paper adapts Gouriéroux and Monfort's (2021) model risk framework to property and casualty insurance, focusing on policy-level data. It addresses model risk at two levels: the impact on predictions and out-of-sample uncertainty, and the need to account for risk during model selection.

This paper explores optimal insurance contracting for a decision maker facing ambiguous loss distributions. Using a p-Wasserstein ball around a benchmark distribution and a convex distortion risk measure, the indemnity function and worst-case distribution are derived. Numerical examples highlight the sensitivity of worst-case distributions to model parameters.Distributionally robust insurance under the Wasserstein distance