“This study presents a structured workflow applying text analysis to operational risk event descriptions. It identifies managerial clusters causing risks, enhancing traditional quantitative methods, and improving risk mitigation based on historical loss events.”
“The study demonstrates the capability of certain public sector banks to bear operational risk on a particular level of regulatory capital. The ability of a bank to be successful under unfavorable conditions is related to its operational risk, regulatory capital and management processes.”
The increasing complexity of data protection laws, rising compliance costs, and evolving cyber threats make data security a vital business concern.
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.
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.
Information about #fdic #insurance and communication about #bank #stability by the #federalreserve can reassure depositors and mitigate #bankruns, while communication from political leaders only influences their electoral base.
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.
#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.
"#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."
This paper presents a fundamental #mathematical duality linking utility transforms and #probability distortions, which are vital in #decisionmaking under #risk. It reveals that these concepts are characterized by commutation, allowing for simple axiomatization with just one property. Additionally, rank-dependent utility transforms are further characterized under monotonicity conditions.