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The paper delves into the intertwining of financial institutions and environmental concerns, particularly climate change and biodiversity loss. It introduces a dual framework based on 'impact' and 'risk' to explore their complex relationship. It analyzes the co-existing but sometimes opposing approaches at their interface, elucidating how finance, climate change, and biodiversity intertwine in the realm of "sustainable finance".
The paper explores optimal insurance contracts using decision makers' preferences, combining expected loss with a deviation measure like Gini coefficient or standard deviation. It reveals that using expected value principle favors stop-loss indemnities, defining precise deductibles. The optimal indemnity structure remains consistent even with a capped insurance premium. Multiple examples based on Gini coefficient and standard deviation illustrate these findings.
“I show that, during a normal economic period, rather than having a disciplining effect, disclosure leads to banks increasing risk taking, consistent with banks facing pressure to offset the costs of stress testing.“
The era of big data revolutionizes operational management in enterprises, amplifying the challenges for auditors in managing vast corporate information and escalating fraud risks. This study explores machine learning's role in identifying financial fraud, constructing models based on fraud triangle theory and empirical data. The model, particularly LightGBM, achieves a 73.21% accuracy, showcasing its effectiveness in predicting fraud risks in publicly traded companies.
The critical role of risk management in organizational success is highlighted, particularly for small and medium-sized enterprises (SMEs) facing unique challenges due to limited resources. "Risk Management by Design" advocates integrating risk management into all facets of SME operations, aiding early identification, cost efficiency, and a competitive edge. Despite benefits, SMEs must navigate resource constraints to effectively balance risk and innovation.
This study explores how AI affects auditors' judgments on complex estimates. It finds that when clients use AI for estimates, auditors' planned responses don't match their risk assessments. Auditors tend to plan less (more) work if AI-generated estimates were more (less) accurate previously, potentially posing concerns about audit effectiveness due to automation bias.
Determining liability for AI-caused harm lacks clear answers. In cases involving Tesla autopilot accidents, unfair discrimination by AI in HR, or medical procedures, responsibility is blurry. The EU's proposed AI liability regime and AI Act aim to address these complexities, bridging gaps in risk regulation and liability for AI-human interactions.
"An adverse development cover (ADC) is a form of an excess of loss reinsurance contract that provides coverage for future loss payments relating to claims incurred prior to a specified date… A framework for assessing the value of an ADC from the perspective of the ceding insurer is developed. This value assists in making decisions regarding the acquisition of an ADC, comparing available options on offer and accounting for the ADC under the IFRS17 accounting standard."
Attack graphs visually map potential attack paths in systems, aiding systematic vulnerability exploration. Enhancing them with countermeasures and consequences streamlines risk assessment, accommodating system or environment changes. Demonstrated through a case study, this method integrates with existing standards, addressing evolving threats for better risk management in computer systems.
"Since the global financial crisis of 2007–9, legal risk has become increasingly important for the banking sector. In Poland, the growth in importance is predominantly associated with the so-called regulatory tsunami, which has seen a constantly changing legal framework for bank operations..."