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The study presents an innovative approach for flood disaster risk assessment and ecological monitoring using Bayesian networks (BNs) and geographic information systems (GIS). By integrating diverse data sources, the BN-GIS model provides a holistic risk profile of flood-prone urban ecosystems in Yinchuan, China. This approach allows for mapping vulnerable hotspots, quantifying uncertainties, and informing sustainable urban planning and disaster resilience efforts.
The modern auditing process aligns with Bayesian methods, enabling auditors to use tools and techniques for targeted audit procedures and assurance modeling. This approach integrates sampling results and supports intuitive, easy-to-implement models for field use. The model generalizes the audit risk model, applying Bayesian techniques and the Stringer posterior.
“... we provide evidence that setting the risk-weighted ratio at 15\% and the simple leverage ratio at 10% would significantly decrease the probability of default without hampering banks' activities.”
The study investigated how ChatGPT and Bard categorize investor risk profiles compared to financial advisors. While there were no significant differences in the risk scores assigned by the chatbots and bankers for half of the clients, the chatbots often missed clients' specific characteristics. Chatbots may serve as a second opinion but cannot replace bankers.
The paper suggests that companies developing high-risk AI systems should demonstrate their safety before deployment, arguing for proactive risk management. It proposes a risk management approach where developers must provide evidence that risks are below acceptable thresholds. The paper discusses technical and operational evidence for safety, comparing its approach to the NIST AI Risk Management Framework.
The article explores the importance of critical infrastructure (CI) and essential services (ES) for population security and business continuity. It examines the challenges posed by the interdependence of CI and ES, which complicates threat identification and risk management. The study identifies new research directions on operational risk management, public security, and resilience in critical supply networks.
The research examines the impact of traditional verification methods on customer satisfaction and operational efficiency in commercial banks. It suggests adopting digital solutions such as biometric authentication and machine learning algorithms to streamline verification, prevent fraud, enhance security, and improve customer engagement. The study aims to provide recommendations for innovative methods aligning with digital transformation and customer expectations.
The model aims to enhance cybersecurity performance and workforce retention through tailored training strategies.
“... the collaborative game provides executives and business leaders with insight into cyber-risk management issues, thereby improving their results through deeper learning.”
“Using cybersecurity certification as the basis for providing a complete defense to liability may not prevent every harm from occurring. However, if organizations invest in certification to avoid legal liability, this should collectively improve the resilience and quality of technology products in the United States and beyond.”