Credit Card Fraud Detection using CNN and LSTM

Authors: Nishant Upadhyay, Abhay Kumar Vajpayee, Divya Rastogi, Rekha Chaturvedi, Mohammad Asim, Suraj Malik, Khel Prakash Jayant, Nidhi Bansal (IJEECS 38373)
Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behaviour of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. 1D CNN and LSTM both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of 2,84,807 transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, f1-score, precision and recall. In our proposed experimental study, getting the f1-score of 99.96% and 99.89% in Ensemble: Early Fusion and Ensemble: Late Fusion respectively.
Indonesian Journal of Electrical Engineering and Computer Science
ijeecs.iaescor...
Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, mee.uad.ac.id #yogyakarta
Admission: mee.uad.ac.id/...
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