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.
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.
This paper analyzes the shift in European digital regulation from a science-based model to one rooted in constitutional values. While traditional risk management relied on the precautionary principle and quantifiable data, modern frameworks like the GDPR, DSA, and AI Act focus on safeguarding fundamental rights and democracy. The authors argue that this transformation addresses the intangible nature of digital harms and the significant imbalance of power between public regulators and private tech firms. By delegating risk assessment to private entities, the EU utilizes accountability and proportionality as tools to govern technological uncertainty. Ultimately, the text illustrates how legal and ethical standards have replaced empirical science as the primary metrics for regulating the digital ecosystem.
This paper provides a rigorous mathematical analysis of the axiomatic foundations used to quantify financial risk. The author traces the evolution of risk measurement from early standards like Value-at-Risk to more sophisticated frameworks including coherent, convex, and spectral risk measures. Central to the text are the representation theorems that define these measures through dual sets of probability scenarios and penalty functions. The scope extends to dynamic settings, where time-consistency is required for multi-period assessments, and systemic risk involving interconnected institutions. Finally, the research bridges the gap between theory and practice by integrating machine learning techniques, specifically examining the concentration of empirical estimators and the use of conformal prediction for distribution-free risk control.
This position paper emphasizes the insurance industry's role as a strategic asset for the continent's economic stability and long-term growth. The organization argues that over-regulation and complex, overlapping legal frameworks currently hinder the sector's ability to invest in European priorities like green technology and infrastructure. To address this, they propose a simplification package and a “Financial Services Omnibus” aimed at reducing administrative burdens and stopping unnecessary new rules. By streamlining requirements for reporting and capital, the industry believes it can better support household savings and enhance the global competitiveness of the Single Market. Ultimately, this paper serves as a formal call for EU leaders to prioritize regulatory efficiency to ensure that insurance providers can continue to anchor Europe’s financial resilience.
This paper by Caroline Hillairet, Olivier Lopez and Lionel Sopgoui (CREST, UMR CNRS) describes a stochastic SIR model designed to quantify the financial impact of contagious cyber-attacks on corporate revenues and insurance portfolios. By blending epidemiological frameworks with economic granular growth models, the researchers account for the reality that larger firms are more frequent targets and exhibit different internal infection dynamics. The model specifically utilizes Cox-Ingersoll-Ross (CIR) processes to incorporate environmental variability, allowing for more realistic simulations of how ransomware spreads within and between organizations. A key practical application analyzes the 2024 LockBit ransomware attacks, offering insurers a method to calculate Aggregate Exceedance Probabilities to forecast potential losses. Ultimately, the framework bridges the gap between cybersecurity technicalities and financial risk management, providing a tool for measuring systemic cyber threats across diverse industrial sectors.
This research introduces a Bayesian Network simulation model designed to quantify the effectiveness of Zero Trust Architecture (ZTA) within small-medium businesses (SMBs). By utilizing Monte Carlo simulations and historical data, the study validates how ZTA can reduce the likelihood of data breaches and the overall magnitude of cyber risk by up to 20 percent. The authors analyze critical implementation barriers, such as financial constraints and organizational resistance, providing a roadmap for resource-strapped firms to adopt "never trust, always verify" principles. Key findings highlight that credential-based attacks and insider threats are the most significant risks, which can be mitigated through core controls like encryption and multi-factor authentication. Ultimately, the model serves as a risk-informed decision tool to help SMBs enhance their cyber resilience and regulatory compliance.
This discussion paper explores strategies for creating a more integrated data collection system for the insurance and pension sectors. The document seeks stakeholder feedback on reducing regulatory reporting inefficiencies, such as redundant data requirements and inconsistent definitions across various EU frameworks. While the insurance sector already benefits from a highly harmonized system under Solvency II, the paper notes that occupational pension (IORPs) reporting remains fragmented and varies significantly by country. Key priorities include streamlining the reporting of derivatives and collective investment undertakings by potentially leveraging existing data sources like EMIR. Ultimately, the initiative aims to lower compliance costs for firms and modernize the digital infrastructure used for supervisory data sharing.
This position paper outlines Insurance Europe’s feedback on the European Commission’s Digital Omnibus initiative, which seeks to streamline the complex regulatory environment for the insurance sector. The organization advocates for reducing administrative burdens by harmonizing rules across artificial intelligence, data protection, and cybersecurity. Key recommendations include delaying specific AI Act obligations to ensure technical readiness and clarifying GDPR definitions to foster innovation in automated decision-making. Additionally, the sources highlight the importance of a Single-Entry Point for reporting cyber incidents and the potential benefits of a European Business Wallet for secure digital authentication. Ultimately, the federation seeks a more coherent legislative framework that balances robust consumer protection with the operational flexibility needed for insurers to remain competitive.
The paper presents a framework for individual claims reserving based on the projection-to-ultimate (PtU) method as an alternative to the traditional chain-ladder approach. It describes how reserving can shift from aggregate loss triangles to claim-level modeling by directly estimating ultimate claim costs. The approach is presented as compatible with classical actuarial structures while enabling the use of stochastic covariates and machine learning models, including neural networks and transformers. The authors emphasize decomposing reserves into Reported But Not Settled (RBNS) and Incurred But Not Reported (IBNR) components to maintain consistent claim cohorts. Case studies suggest that linear regression can perform robustly in individual-claim settings.