Credit Card Fraud Detection with Advanced Hybrid Deep Learning Algorithm

Progress in the rise of online payments systems has made shopping easier. However, it also increased credit card fraud, with losses expected to exceed $362 billion by 2027. Traditional machine learning algorithms often fail to detect fraud due to complex patterns and unbalanced data. This study uses the IEEE-CIS dataset over which machine learning (ML) and deep learning (DL) algorithms such as Decision Trees, XGBoost, LightGBM, and an advanced Hybrid Deep Learning Model were applied for credit card fraud detection. The proposed model gave the best results, with an ROC AUC of 0.9709 and F1-score of 0.9103. The paper suggests that combining deep learning with explainable AI can improve fraud detection, and future research will aim to make models more transparent and efficient.