Learning Inter‑Annual Flood Loss Risk Models from Historical Flood Insurance Claims

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 Credit Suisse CoCo Wipeout: Facts, Misperceptions, and Lessons for Financial Regulation

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.

Correlation Pitfalls With ChatGPT: Would You Fall for Them?

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"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."

Measuring Ai Safety

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.

Multivariate risk measures for elliptical and log‑elliptical distributions

"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."