Real-Time Detection of Credit Card Fraud in Online Payment Practices: A Deep Learning Methods

The detection of fraudulent credit card transactions has become more challenging due to the growing reliance on digital payment systems. This is especially true in situations with huge financial data dimensions and major class imbalances. A Convolutional Neural Network (CNN) based efficient credit card fraud (CCF) management system is proposed in this paper as a solution to this problem. The CNN can identify intricate and non-linear transaction patterns from transaction data. Experimentation was performed on the European cardholder credit card fraud data retrieved on Kaggle, which is highly imbalanced, and it mirrors the real-life behavior of transactions. Data cleanup, minmax normalization, feature selection using Principal Component Analysis (PCA), and class balance using the Synthetic Minority Oversampling Technique (SMOTE) were all steps in a lengthy data pretreatment pipeline. The data used for training and testing were divided 70:30. Used F1-score (F1), recall (REC), accuracy (ACC), and precision (PRE) to measure the suggested CNN model as well. Based on the experimental data, the CNN model outperforms some of the existing ML and DL models with 94.8% ACC, 93.9% PRE, 95.6% REC, and 94.7% F1. The results back up claims that a CNN-based system can detect CCF in online payment systems in real-time and is reasonable, reliable, and scalable.