41 résultats
pour « operationalrisk »
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 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."
"The article provides a short overview of methods for constructing mathematical models in the form of Bayesian Networks for modeling operational risks under conditions of uncertainty. Let’s provide the sequence of actions necessary for creating a model in the form of the network, methods for computing a probabilistic output in BN, and give examples of using the tool to solve practical problems of operational financial risk estimation."
"... the methods discussed in this paper can apply to general machine learning classifiers in applications with imbalanced data issues, by using a case study in credit card fraud detection this paper calls practitioners’ attention to the imbalanced data problems therein, where class imbalance is often mistreated and lacks theoretical discussion."
"... we propose an approach to estimate very large losses similar to that used by Fermi and Drake to estimate the existence of extraterrestrial life. It consists of supposing the event of interest is the result of a concatenation of independent factors and estimating the probability of each factor. The problem is that the events in the causal chain might be events that have never been observed, which ties our subject to that of the estimation of probabilities of rare events."
"This paper ... documents some of the most prominent cases of misconduct, which it summarizes in terms of operational risk losses (using Turner’s framework for analyzing organizational disasters) and also details some egregious examples of operational risk events ..."
"... an overview of how machine learning can help in categorizing textual descriptions of operational loss events into Basel II event types. We apply PYTHON implementations of support vector machine and multinomial naive Bayes algorithms to precategorized Öffentliche Schadenfälle OpRisk (ÖffSchOR) data to demonstrate that operational loss events can be automatically assigned to one of the seven Basel II event types with very few costs and satisfactory accuracy."
"... 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 have applied text analysis methodologies to extract information from descriptions in the OpRisk database. After delicate tasks like data cleaning, text vectorization, and semantic adjustment, we apply methods of dimensionality reduction and several clustering models and algorithms to develop a comparison of their performances and weaknesses. Our results improve retrospective knowledge of loss events and enable to mitigate future risks."
"... operational risk remained the most prominent major risk type after the outbreak of Covid-19, and that disclosures of operational risk increased by 5.19% compared with the samples from before the outbreak. The drivers of operational risk also changed, with significant increases in disclosure of litigation risk, transaction modes and product and service problems as a proportion of total disclosures. In addition, two emerging operational risk drivers identified during the pandemic are data safeguarding and goodwill impairment."