Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset
Various problems of any credit card fraud detection based on machine learning
come from the imbalanced aspect of transaction datasets. Indeed, the number of
frauds compared to the number of regular transactions is tiny and has been
shown to damage learning performances, e.g., at worst, the algorithm can learn
to classify all the transactions as regular. Resampling methods and
cost-sensitive approaches are known to be good candidates to leverage this
issue of imbalanced datasets. This paper evaluates numerous state-of-the-art
resampling methods on a large real-life online credit card payments dataset. We
show they are inefficient because methods are intractable or because metrics do
not exhibit substantial improvements. Our work contributes to this domain in
(1) that we compare many state-of-the-art resampling methods on a large-scale
dataset and in (2) that we use a real-life online credit card payments dataset.