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Various problems of any credit card fraud detection based on machine learningcome from the imbalanced aspect of transaction datasets. Indeed, the number offrauds compared to the number of regular transactions is tiny and has beenshown to damage learning performances, e.g., at worst, the algorithm can learnto classify all the transactions as regular. Resampling methods andcost-sensitive approaches are known to be good candidates to leverage thisissue of imbalanced datasets. This paper evaluates numerous state-of-the-artresampling methods on a large real-life online credit card payments dataset. Weshow they are inefficient because methods are intractable or because metrics donot exhibit substantial improvements. Our work contributes to this domain in(1) that we compare many state-of-the-art resampling methods on a large-scaledataset and in (2) that we use a real-life online credit card payments dataset.

Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset