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.
The article delves into #ethical concerns with #aitools in #legal and #tax research, addressing #output #quality, #bias, #verifiability, #liability, and #privacy #risks. It explores #regulatory, #tech, and professional solutions, offering practical advice for tax professionals to safely navigate AI's challenges with #riskmitigation.
#cybersecurity goes beyond networks and people, encompassing #physicalsecurity crucial for organizations. Inadequate physical security, seen in incidents like the Oklahoma City bombing, 9/11 attacks, and U.S. Capitol breach, highlight policy and control failures. Effective physical security involves planning, #riskassessment, #controls, and frameworks like #cpted, #nist, and #fema, addressing present and future #threats.
The essential role of #ai in #banking holds promise for efficiency, but faces challenges like the opaque "black box" issue, hindering #fairness and #transparency in #decisionmaking #algorithms. Substituting AI with Explainable AI (#xai) can mitigate this problem, ensuring #accountability and #ethical standards. Research on XAI in finance is extensive but often limited to specific cases like #frauddetection and credit #riskassessment.
The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$-divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$-divergence-derived sets yield strong time-consistency, while Wasserstein distance leads to a new non-normalized time-consistency. Recursive representations of one-step conditional robust risk measures underlie strong or non-normalized time-consistency.