Abstract
As credit card becomes the most popular payment mode particularly in the online sector, the fraudu- lent activities using credit card payment technologies are rapidly increasing as a result. The purpose of this work is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism Long Short Term Mem- ory(LSTM) deep Recurrent Neural Networks(RNN) and Synthetic Minority Oversampling Technique Edited Nearest Neighbour(SMOTE-ENN). The pro- posed approach aims to capture the historic purchase behavior of credit card holders with the goal of im- proving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high. keywords: Credit Card, SMOTE-ENN, FraudDetection, Machine learning,LSTM.
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