Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization
This addresses fraud detection in European banking by reducing unnecessary human workload, though it is incremental as it adapts existing methods to a new scenario.
The paper tackles the problem of fraud detection in banking transactions by shifting focus from improving classifier accuracy to minimizing the number of non-fraudulent operations submitted for human verification, showing that a simple meta-algorithm achieves competitive results on benchmark datasets.
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.