Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data. (arXiv:2208.11904v1 [cs.LG])

With growing credit card transaction volumes, the fraud percentages are also
rising, including overhead costs for institutions to combat and compensate
victims. The use of machine learning into the financial sector permits more
effective protection against fraud and other economic crime. Suitably trained
machine learning classifiers help proactive fraud detection, improving
stakeholder trust and robustness against illicit transactions. However, the
design of machine learning based fraud detection algorithms has been
challenging and slow due the massively unbalanced nature of fraud …

data lg machine machine learning metrics study