This study reveals how operational risk events affect US bank CEO compensation from 1992-2016. Results indicate that compensation committees take operational risk into account & that recent regulations have enhanced this process. Additionally, operational risk events have a detrimental effect on options-based compensation.
"We study blockchain adoption in insurance-reinsurance markets. Operating costs decrease with the adoption rate, since verification and storage costs are shared. We quantify how the equilibrium adoption decisions depend on contract characteristics, risk aversions, potential losses and cost structure. The reinsurance firm internalizes the benefits of adoption on other insurance firms, acting as a central planner. We characterize the adoption gap between decentralized (Nash) and centralized blockchain consortium."
"We aim to analyze strategies for assessing and managing new risks that affect the insurance industry, considering the regulatory requirements that the company must follow. To this end, the open-source software Climada was examined. This software uses stochastic forecasting models such as ARCH, GARCH, and ARIMA. Through real data obtained during an internship at E&Y, it was determined that these models can be a useful tool for insurance companies when dealing with extreme risks. This includes their exposure and solvency. Additionally, the study explores issues related to climate change"
"By employing Big Data and Artificial Intelligence (AI), personal data that is categorized as sensitive data according to the GDPR Art. 9 can often be extracted. Art. 9(1) GDPR initially forbids this kind of processing. Almost no industrial control system functions without AI, even when considering the broad definition of the EU AI Regulation (EU AI Regulation-E)."
"Machine learning methods are getting more and more important in the development of internal models using scenario generation. As internal models under Solvency 2 have to be validated, an important question is in which aspects the validation of these data-driven models differs from a classical theory-based model."
"We respond to Tetlock et al. (2022) showing 1) how expert judgment fails to reflect tail risk, 2) the lack of compatibility between forecasting tournaments and tail risk assessment methods (such as extreme value theory). More importantly, we communicate a new result showing a greater gap between the properties of tail expectation and those of the corresponding probability."
"This study proposes a comprehensive method (with representative AI-Technologies as a data basis) for the structured and targeted categorization and classification of AI under the risk-based audit approach. Initial feedback received by AI-Experts regarding the design and development of the artifact is collected. With the developed method, the study contributes to the descriptive and prescriptive knowledge base regarding the categorization and classification of AI within the auditing and accounting profession."
"Bayesian estimates from experimental data can be influenced by highly diffuse or "uninformative" priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments of decision-making under risk, and discuss some implications of the findings."
"We show that past operational losses are informative of future losses, even after controlling for a wide range of financial characteristics. We propose that the information provided by past losses results from them capturing hard to quantify factors such as the quality of operational risk controls, the risk culture, and the risk appetite of the bank."
"... we study the behavior of the asymptotic tail distribution of independent sums of heavy-tailed random vectors under the paradigm of multivariate regular variation. Assessment of such tail probabilities are of interest in risk management for many finance, insurance, queueing, and environmental applications. Multidimensional tail events are often characterized by at least one variable exceeding a high threshold, and the asymptotic probability of such events follow the so-called “one large jump” principle..."