A Comparison between Sustainability Frameworks

Implementing Agenda 2030 and its global Sustainable Development Goals (SDGs) requires a concerted effort from institutions and the private sector. Sustainable Finance plays a crucial role in achieving this. International directives like Sustainability Reporting are shaping the landscape, emphasizing ESG criteria. This paper compares various sustainability frameworks and highlights the importance of ESG criteria for sustainability analyses and portfolio selection. It also suggests an integrated ERM framework to align sustainability with financial decisions, enhancing coherence with SDGs and facilitating cross-framework integration.

Generative AI and the Workforce: What Are the Risks?

This paper finds that 38.9% of tasks in jobs involve large language models, with 80% of workers spending 20% of their time on such tasks.Its mapping of risk exposure shows that LLMs directly expose 12.4% of tasks to privacy risks, 13.7% to cybersecurity risks, 13.6% to breach in professional standards risks, 14.1% to unethical or harmful bias risks, 10.6% to misinformation and manipulation risks, 26.4% to safety and physical harm risks, 26% to liability and accountability risks and 9.8% to intellectual property risks.

On Risk Management of Mortality and Longevity Capital Requirement: A Predictive Simulation Approach

In the insurance sector, life insurers must meet capital requirements to avoid insolvency risks, especially during events like the COVID-19 pandemic. Risk management and risk mitigation are crucial. This paper presents an efficient simulation method, a thin-plate regression spline, as an alternative to nested simulations, to explore hedging strategies using mortality-linked securities and stochastic mortality dynamics. The results justify the use of mortality-linked securities in risk management and risk mitigation for capital associated with mortality and longevity.

Natural Disaster Risk and Firm Performance: Text Mining and Machine Learning Approach

Advanced #machinelearning models were found to be more effective than #linearregression in predicting firm performance under #naturaldisaster #risks. The study suggests that textual data in #financialreports can be used to measure the perceived natural disaster risk and predict its effects on firm performance.

Evolution of Cybersecurity Disclosure

#regulators recently issued #cybersecurity #disclosure guidelines to enhance #transparency and #accountability among firms. A study analyzed cybersecurity disclosure practices among a sample of Toronto Stock Exchange firms over seven years. Findings indicate a notable increase in disclosure after 2017 guidance by #canadian Securities Administrators. However, improvements are needed, especially in #governance and #riskmitigation disclosure. This study sheds light on policy's impact on cybersecurity transparency.

Deep Semi‑Supervised Anomaly Detection for Finding Fraud in the Futures Market

"#frauddetection is overwhelmingly associated with the greater field of #anomalydetection, which is usually performed via unsupervised learning techniques because of the lack of labeled data needed for #supervisedlearning. However, a small quantity of labeled data does often exist. This research article aims to evaluate the efficacy of a deep semi-supervised anomaly detection technique, called Deep SAD, for detecting #fraud in high-frequency #financialdata."