"#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.
Examining #us #bank holding companies, this research analyzes how #audit committee oversight influences financial reporting quality. Leveraging Section 165 h of the Dodd–Frank Act, which mandates separate #audit and #risk committees for large bank holding firms, the study employs a difference-in-differences approach. The separation leads to enhanced reporting quality due to improved audit committee focus stemming from reduced task complexity post-Section 165 h.
#auditors fail to detect material weaknesses in clients' #internalcontrols over #financialreporting (#icfr) when clients experience financially material negative #esg incidents, which eventually lead clients to restate their financial statements. The results suggest that auditors overweight their clients' attempts at improving their internal controls following the revelation of material ESG incidents.
This study examines a #riskaverse #insured who buys deductible #insurance and uses a barrier strategy for reporting #losses. The #insurer has a bonus-malus system with two rate classes; shifting to a costlier class occurs upon loss reporting. The insured's tendency to underreport losses is established under specific conditions, with her strategic reporting threshold derived. Allowing insureds to choose deductibles reveals positive equilibrium values, challenging the assumption of full insurance optimality. This work explains the common underreporting of losses across non-life insurance sectors.
"We study a #reinsurer who faces multiple sources of #model #uncertainty. The reinsurer offers contracts to n #insurers whose #claims follow different compound #poisson processes. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an #entropy #penalty…."