30 résultats
pour « machinelearning »
"... 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."
"... 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."
"... blockchain networks are vulnerable to anomalies and frauds that can have serious consequences for the integrity and security of these networks. In this primer, we provide an overview of the definition and properties of blockchain technology, and discuss the types and examples of anomalies and frauds that occur in these networks."
"... we develop a framework based on a combination of a neural network together with a dimensionality reduction technique t-SNE (t-distributed stochastic neighbour embedding)... The obtained results, which are based on real insurance data, reveal a clear contrast between the high and low risk policy holders, and indeed improve upon the actual risk estimation performed by the insurer."
"... if enacted as foreseen, AI liability in the EU will primarily rest on disclosure of evidence mechanisms and a set of narrowly defined presumptions concerning fault, defectiveness and causality."
"... supply chain network features add significant detection power to predicting enterprise cyber risk, relative to merely using enterprise-only attributes. Particularly, compared to a base model that relies only on internal enterprise features... Given that each cyber data breach is a low probability high impact risk event, these improvements in the prediction power have significant value."
"This paper reviews the use of AI in the ESG field: textual analysis to measure firms’ ESG incidents or verify the credibility of companies’ concrete commitments, satellite and sensor data to analyse companies’ environmental impact or estimate physical risk exposures, machine learning to fill missing corporate data (GHG emissions etc.)."
"These attacks are unknown to the human eye due to malicious intent to harm any underlying infrastructure. So, to overcome the problems and make a flexible solution, we propose a framework where machine learning algorithms are applied to find relevant features from the existing dataset."
" Predictive machine learning algorithms used in banking environments, especially in risk and control functions, are generally subject to regulatory and technical constraints limiting their complexity. Knowledge distillation gives the opportunity to improve the performances of simple models without burdening their application, using the results of other - generally more complex and better-performing - models."
"We conclude that users of the Scope 3 emission datasets should consider data source, quality and prediction errors when using data from third party providers in their risk analyses."