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The Q4 2025 EBA Risk Dashboard summarizes the European banking sector’s condition using a "traffic light" Risk Indicator heatmap. The report describes a period of high liquidity, noting that no sampled assets fell into the highest risk category for Liquidity Coverage Ratios. While solvency remains strong, with 79% of assets in the top Tier 1 capital bracket, this reflects a slight decrease from 2024. Profitability remains a concentrated risk, as nearly half of assets show high cost-to-income ratios. Overall sector stability is monitored through asset-weighted indicators and a composite Risk Assessment meter.
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This research presents a machine learning framework designed to predict and reduce the risk of identity theft caused by phishing and social engineering. The authors developed a Cyber Risk Score (CRS) that combines observable security habits, like password hygiene, with latent psychological traits such as impulsive link-clicking. By utilizing a hybrid stacking ensemble model, the study achieved a 93% accuracy rate in identifying vulnerable social media users. Beyond mere prediction, the system uses SHAP analysis to provide transparent, personalized recommendations tailored to an individual’s specific behavioral weaknesses. This user-centered approach aims to bridge the gap between cybersecurity knowledge and actual online behavior through evidence-based interventions. Ultimately, the framework offers a scalable, ethical solution for organizations to protect users in increasingly sophisticated digital environments.
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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.