By analyzing the content of #rating#reports, the study identifies seven different mechanisms that rating analysts are concerned about, with the most significant being the #misstatement-related violations of #debt covenants that increase #liquidityrisk and #compliancerisk. The study finds that #creditratings and #reputationalrisk of #misreporting firms are adversely affected for up to seven years after an intentional misstatement becomes publicly known. The impact of an intentional misstatement on a firm's credit rating is most pronounced when rating analysts express concerns about covenant violations.
This study leverages prospective memory theory to examine how encouraging #auditors to have implementation intentions can improve their attention to #fraud. Results suggest that encouraging implementation intentions, can increase auditors’ attention to fraud and make them more likely to take effective #antifraud actions. However, even in high fraud risk settings, auditors may still devote insufficient attention to fraud when performing planned #audit procedures, raising concerns for #regulators.
In #corporategovernance, where boards are being held liable for #misconduct based on #operationalrisk. Operational misconduct is a critical source of #director#liability and should be given the same attention as #financial#mismanagement. Operational risk marks a fundamental shift in the way boards monitor the firm. Judicial doctrine is changing the way boards manage operational risk, avoid liability, and protect stakeholders' lives and the society at large.
The paper discusses the proposed #climatechange #disclosure rules by the #sec, which would mandate companies to provide detailed disclosures on the impact of climate change on their financial performance and policies. The authors conducted a study using hand-collected data from 99 annual reports of 34 S&P 500 companies from 2019 to 2021, finding that 91% of the #annualreports included some disclosures on climate-related risks. The study found a positive relationship between climate-related disclosures and firms’ financial performance.
This paper focuses on the development of #bayesian classification and regression tree (#cart) models for claims frequency modeling in non-life #insurance pricing. The authors propose the use of the zero-inflated #poisson distribution to address the issue of imbalanced claims data and introduce a general MCMC algorithm for posterior tree exploration. Additionally, the deviance information criterion (DIC) is used for model selection. The paper discusses the applicability of these models through simulations and real insurance data.
This paper advocates for the use of #sandbox#regulation to complement a strict #liabilityregime in regulating #artificialintelligence (#ai). The #eu#regulators have already embraced this concept.
This study focuses on the #investmentdecisions and #riskattitudes of #generationz university students in #hungary. Generation Z is looking for secure, fast, and easy #financialsolutions and services that are virtually available at any time from their #mobilephones.
The paper discusses the risks posed by #artificialintelligence (#ai) systems, from biased lending algorithms to chatbots that spew violent #hatespeech. The author argues that policymakers have a responsibility to consider broader, longer-term #risks from #aitechnology, such as #systemicrisk and the potential for misuse. While #regulatory proposals like the #eu #aiact and the #whitehouse AI Bill of Rights focus on immediate risks, they do not fully address the need for #algorithmicpreparedness. It proposes a roadmap for algorithmic preparedness, which includes five forward-looking principles to guide the development of regulations that confront the prospect of algorithmic black swans and mitigate the harms they pose to society. This approach is particularly important for general purpose systems like #chatgpt, which can be used for a wide range of applications, including ones that may have unintended consequences. The article emphasizes the need for #governance and #regulation to ensure that #aisystems are developed and used in ways that minimize risk and maximize benefit, and it references the #nist AI #riskmanagement Framework as a potential tool for achieving this goal.
This paper discusses #decentralized#insurance and its various forms of #risksharing mechanisms developed worldwide. It highlights the need for a unified #mathematical framework to describe the commonalities and relationships between different forms of #peertopeer insurance. The framework allows for a comparison of existing practices and the design of hybrid and innovative #models .
This article applies the Insurance Performance Measure (IPM) model to a set of #indian#insurance companies over the period 2005-2016, which is the first study to apply this model on real industry data. The IPM was introduced as a better way to assess industry and company performance for insurance companies as traditional #financialaccounting analysis is not suitable for the unique format of insurance company financials. IPM incorporates #underwriting, investment, and #reinsurance along with a hurdle rate and is consistent with Warren Buffett's desire for a balanced overview of industry performance. The model could help in identifying the threshold limit for overall profitability and in negotiations for reinsurance renewals.Read