Wenxuan Shi
ABSTRACT
The great expansion of data availability gives rise to some consumer financial problems, including credit card fraud. It is an urge to look into this fraudulent behavior and try to make successful detection, so the continuous analysis from raw data is necessary [1]. It should be noted that it is hard to get the original dataset because it will involve personal information. Besides, credit card data fraud transition tends to be highly imbalanced. In this paper, the experiment is based on one credit card dataset, and I mainly assess the performance of three classification models: logistic regression, random forests and boosted regression trees, which are commonly used under imbalanced situation. Sampling methods are also used on the data for a better result. The experiment adopts AUC, H-measure and detection rate as assessment metrics, trying to figure out the best classification model for the credit card fraud.
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