"This paper reviews the use of AI in the ESG field: textual analysis to measure firms’ ESG incidents or verify the credibility of companies’ concrete commitments, satellite and sensor data to analyse companies’ environmental impact or estimate physical risk exposures, machine learning to fill missing corporate data (GHG emissions etc.)."
"We argue that datafication of insurer processes may fuel excessive data collection in the context of insurance contracts, generating a substantial risk of harm to consumers, especially in terms of discrimination, exclusion, and unaffordability of insurance. "
"Using a large sample of U.S. firms over the period 2007-2017, we find that when cybersecurity risk is higher, firms hold more cash."
"Our study contributes to the brainstorming literature by highlighting the importance of group composition. It suggests that firms can promote skeptical team judgments by leveraging individuals’ high trait skepticism in thoughtfully composed interacting groups."
" The study focused on evaluating and analysing the characteristics of literature and the themes investigated with a focus on four key aspects: governance, the effectiveness of IA, the relationship between internal auditors and other parties, and risk management to provide directions for future research."
"We find that bank capital and probability of default PD impact each other, where the total influence of the latter on the former is stronger. PD also affects capital via risk-taking but the opposite effect (i.e., from capital to PD via risk) is not identified, which challenges one of the main assumptions underlying capital regulation."
"After shortly summarising the origin, context and main characteristics of the prospective regulation, this article explores whether the ‘Brussels Effect’ will manifest in ground-breaking AI regulation, or whether the Union and its Member States run the risk of hastily adopting an incapable legal framework for a technology whose effects on society are still insufficiently understood."
"Insurance fraud has been a long-lasting issue in actuarial modeling. Policyholders are prone to hide their true status in their best interest when disclosing their information for insurance pricing purposes. However, from the insurers' point of view, it is either time-consuming or laborious to verify the true status of such risk factors. There is thus a strong incentive to build models accounting for potential misrepresentation, which contributes to a more robust ratemaking system."
"These attacks are unknown to the human eye due to malicious intent to harm any underlying infrastructure. So, to overcome the problems and make a flexible solution, we propose a framework where machine learning algorithms are applied to find relevant features from the existing dataset."
" Adopting a risk-based approach towards AI, the EU chose to understand trustworthiness of AI in terms of the acceptability of its risks. This conflation of trustworthiness with acceptability of risk invites further reflection. Based on a narrative systematic literature review on institutional trust and the use of AI in the public sector, this paper argues that the EU adopted a simplistic conceptualisation of trust and is overselling its regulatory ambition."