| Abstract: | With the wide usage of e-banking in recent years, and by increased opportunities for fraudsters subsequently, we are witnessing a loss of billions of Euros worldwide due to credit card fraud every year. Therefore, credit card fraud detection has become a critical necessity for financial institutions. Several studies have used machine learning techniques for proposing a method to address the problem. However, most of them did not take into account the sequential nature of transactional data. In this paper, we proposed a novel credit card fraud detection model using sequence labelling based on both deep neural networks and probabilistic graphical models (PGM). Then by using two real-world datasets, we compared our model with the baseline model and examined how considering hidden sequential dependencies among transactions and also among predicted labels can improve the results. Moreover, we introduce a novel undersampling algorithm, which helps to maintain the sequential patterns of data during the random undersampling process. Our experiments demonstrate that this algorithm achieves promising results compared to the state-of-the-art methods in oversampling and undersampling. |
