Well-capitalized #banks play a crucial role in supporting economic adaptation to #weather-induced #labor #productivity #risk.
This research evaluates different regression models to predict #flood-induced #insuranceclaims, using the #us #national #floodinsurance Program (#nfip) dataset from 2000 to 2020. The models studied include #neuralnetworks (Conditional Generative Adversarial Networks), #decisiontrees (Extreme Gradient Boosting), and #kernel-based regressors (#gaussian Process). The study identifies key predictors for regression, highlighting factors that influence flood-related financial damages.
The #creditsuisse #coco wipeout occurred when the #finma announced that the contingent convertible bonds that were part of the Credit Suisse Additional #tier1 (AT1) #regulatory capital had been written off.FINMA’s decision creates a healthy precedent: restoring #financialdiscipline in AT1 #bondmarkets by reminding investors that their investment is exposed to #creditrisk and that #duediligence is advised before investing in these products.
"We use #naturallanguageprocessing to #measure #supplychainrisk (#scr) faced by #us firms, as expressed in narratives of quarterly earnings conference calls."
"#ifrs17 introduces the concept of a #riskadjustment that compensates #insurers for the #uncertainty about the amount and timing of the cash flows that arise from #nonfinancial#risks. The method for its calculation is not prescribed and several options are emerging, including #var and cost of #capital."
"This paper presents an intellectual exchange with #chatgpt, … , about correlation pitfalls in #riskmanagement. … Our findings indicate that ChatGPT possesses solid knowledge of basic and mostly non-technical aspects of the topic, but falls short in terms of the mathematical goring needed to avoid certain pitfalls or completely comprehend the underlying concepts."
"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."