Temporal Modelling Of Credit Card Transactions Using Recurrent Neural Networks

The rise in the use of online payment systems has resulted in an increase in the number of cases of credit card fraud, hence the need for an effective and efficient credit card fraud detection system. This paper presents a credit card fraud detection system that uses Recurrent Neural Networks (RNN). The proposed system has the ability to analyze past sequences of transactions and learn time-dependent patterns that can be used to differentiate between fraudulent and genuine transactions. The proposed system is tested on a publicly available credit card transaction dataset, and its performance is measured using standard metrics such as AUC, F1-score, recall, precision, and accuracy. The results of the experiment demonstrate that the RNN is effective in the detection process and minimizes both false positives and false negatives, thereby establishing its applicability in real-world financial fraud detection systems.