  The automatic detection of frauds in banking transactions has been recently
studied as a way to help the analysts finding fraudulent operations. Due to the
availability of a human feedback, this task has been studied in the framework
of active learning: the fraud predictor is allowed to sequentially call on an
oracle. This human intervention is used to label new examples and improve the
classification accuracy of the latter. Such a setting is not adapted in the
case of fraud detection with financial data in European countries. Actually, as
a human verification is mandatory to consider a fraud as really detected, it is
not necessary to focus on improving the classifier. We introduce the setting of
'Computer-assisted fraud detection' where the goal is to minimize the number of
non fraudulent operations submitted to an oracle. The existing methods are
applied to this task and we show that a simple meta-algorithm provides
competitive results in this scenario on benchmark datasets.
