Risk Aggregation and Allocation in the Presence of Systematic Risk via Stable Laws

Effective risk management requires understanding aggregate risks, individual business unit riskiness, and systemic risks. Realistic models must consider complex phenomena like heterogeneous marginals and excess kurtosis. A modified individual risk model using Multivariate Stable Distributions addresses these challenges, enabling tractable aggregation, dependence analysis, and Tail Conditional Expectation calculations for aggregate risks.

Generative Artificial Intelligence for Finance Professionals

The paper explains Artificial Intelligence (AI), focusing on Generative AI, its role in finance, and its differences from Machine Learning. It covers AI’s applications in financial forecasting, risk management, and decision-making, while addressing benefits, challenges, regulations, and ethical concerns. It offers practical advice for adopting AI technologies in financial operations.Generative Artificial Intelligence for Finance Professionals

Stochastic Loss Reserving: Dependence and Estimation

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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.

Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context

“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.”

An Innovative Attention‑based Ensemble System for Credit Card Fraud Detection

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.

How Ai Systems Reflect, Perpetuate, and Exacerbate Organisational Biases

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.

Crowdfunding Fraud Detection: A Systematic Review Highlights AI and Blockchain using Topic Modeling

“… 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.”

Optimal mutual insurance against systematic longevity risk

"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."

How Does Open Innovation Affect Risk? The Case of Open Source Software Vulnerability Exploitation

Open innovation in software can improve security by allowing vulnerabilities to be found before release. However, for open source software, post-release vulnerabilities are more likely to be exploited due to source code visibility. This research shows that open source software faces greater attack risks after vulnerabilities are discovered compared to closed source software.