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"... we introduce a novel methodology to model rating transitions with a stochastic process. To introduce stochastic processes, whose values are valid rating matrices, we noticed the geometric properties of stochastic matrices and its link to matrix Lie groups."
"... we find the difference in decision-makers’ probability assessments between operational and non-operational risk factors is greater when assessing a proximate rather than a remote target. We contribute to the accounting literature by demonstrating how spatial distance affects probability judgments."
"Using a broad international sample, we find that banks with better governance in countries with better regulatory quality have lower risk. These results are stronger in more developed countries and in countries with less concentrated banking sectors."
"Building on the Liquidity Coverage Ratio created under the Basel III regulatory agreement, this paper introduces the notion of Liquidity Coverage at Risk (LCRisk), which is the probability that a bank becomes insolvent in the next 30-days. LCRisk has a closed-form expression and it can be computed using information contained in the bank’s balance sheet."
"Creating less loss than insurance otherwise might have created is not regulation or loss prevention. Rather, it is damage-control, and that is what insurance devices designed to combat moral hazard almost always involve. Insurers face a daunting set of obstacles to further reducing policyholder risk below what it would be in the absence of insurance."
"The EU’s GDPR and proposed AI Act tend toward a sustainable environment of AI systems. However, they are still too lenient and the sanction in case of non-conformity with the Regulation is a monetary sanction, not a prohibition. This paper proposes a pre-approval model in which some AI developers, before launching their systems into the market, must perform a preliminary risk assessment of their technology followed by a self-certification."
" We show that the U.S. insurance industry’s capacity to pay catastrophe losses is higher in 2020 than it was in 1997. Insurers could pay 98% of a $200 billion loss in 2020 in comparison to 81% in 1997."
"The integration of data in a geographic information system enables the visualization and spatialization of risk, but also each of its components."
"... DL [deep learning] does not outperform traditional ML [machine learning] models in the case of structured datasets with fixed-length feature vectors. Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a one size fits all solution."
"There appears a gap in cyber risk modeling between engineering and insurance literature. This paper presents a novel model to capture these unique dynamics of cyber risk known from engineering and to model loss distributions based on industry loss data and a particular company's cybersecurity profile. The analysis leads to a new tool for allocating resources of the company between cybersecurity investments and loss-absorbing reserves."