"We show that classical #insurance #models based on some compound distributions can well predict #information #leakage by #cyberincidents with reducing the computational cost thanks to the model’s simplicity."
This paper addresses the challenges associated with the adoption of #machinelearning (#ml) in #financialinstitutions. While ML models offer high predictive accuracy, their lack of explainability, robustness, and fairness raises concerns about their trustworthiness. Furthermore, proposed #regulations require high-risk #ai systems to meet specific #requirements. To address these gaps, the paper introduces the Key AI Risk Indicators (KAIRI) framework, tailored to the #financialservices industry. The framework maps #regulatoryrequirements from the #euaiact to four measurable principles (Sustainability, Accuracy, Fairness, Explainability). For each principle, a set of statistical metrics is proposed to #measure, #manage, and #mitigate #airisks in #finance.
"This paper introduces the multivariate range Value-at-Risk (MRVaR) and multivariate range covariance (MRCov) as #risk#measures for #riskmanagement in #regulation and investment… Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples."
#crisis #riskmanagement"The existing data show that #political #crises make #economiccrises crises more likely, so that, as suggested by the concept of #polycrisis, feedback between non-economic crises and economic crises can be important, but there is no comparable evidence for #climate events."
"... we propose applying the #risk categories to specific #ai #scenarios, rather than solely to fields of application, using a #riskassessment #model that integrates the #aia [#eu #aiact] with the risk approach arising from the Intergovernmental Panel on Climate Change (#ipcc) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (#llms) as an example."
"... the combinatorial approach of #ai and #cybersecurity is beneficial but still the associated danger persist owing to its manipulation by #cyberattackers. This chapter highlights the recent AI techniques deployed in cybersecurity and identifies the #ethical challenges thereof."
"We provide guidance on the types of applications for which consulting #chatgpt can be useful to enhance your knowledge about #quantitative #riskmanagement in #actuarial practice, and point out those for which ChatGPT should better not be invoked."
This paper discusses the role of #centralbanks in #regulating and #supervising #esgrisks in the #banking sector. The authors review recent international and regional rules requiring banks to consider #esg factors in their #governance, and analyze the practices of #microprudential #supervisors in several jurisdictions.
"Observed #competitive market #profitmargins in #insurance have generally exceeded what is considered fair being the #capm adjustment for risky loss cashflows. This potential ‘missing link’ has attempted to be explained by either #risk, #capital or frictions that are unrecognised by the theory. It is proposed here that the missing link instead relates to the consumption of insurance services for which a fair profit margin arises under marginal utility principles."
#ethical dilemmas and #regulatory considerations associated with #ai and #chatgpt adoption in financial analysis are ... addressed, emphasizing the need for responsible AI usage and human oversight in critical #financial judgments.