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"The empirical results in this research show that the classification performance of our proposed methodology is superior compared to that of a large number of traditional classifier models. We also show that our proposed methodology solves the limitation of previous bankruptcy models using textual data, as they can only make predictions for a small proportion of companies."
"Despite that some attention or self-attention based models with time-aware or feature-aware enhanced strategies have achieved better performance compared with other temporal modeling methods, such improvement is limited due to a lack of guidance from global view. To address this issue, we propose a novel end-to-end Hierarchical Global View-guided (HGV) sequence representation learning framework. "
"This paper analyzes how governments support insurance markets to maintain insurability and limit risks to society. We propose a new conceptual framework grouping government interventions into three dimensions: regulation of risky activity, public investment in risk reduction, and co-insurance."
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"This implementation in France provides a new framework for non-financial information and marks a major turning point towards greater potential responsibilities for members of the management bodies of the large corporations in question, in particular with the description of diversity policy: non-financial reporting is reviewed via an overall analysis guided by the materiality principle and genuine sustainable compliance based on a prior and relevant risk assessment."
"Using the lab, in numerical case studies, we identify two classes of measures to control systemic cyber risks: security- and topology-based interventions. We discuss the implications of our findings on selected real-world cybersecurity measures currently applied in the insurance and regulation practice or under discussion for future cyber risk control. To this end, we provide a brief overview of the current cybersecurity regulation and emphasize the role of insurance companies as private regulators."