Credit Card Fraud Detection Model Based on Explainable Tabnet-Based Feature Selection

Abstract

Financial losses and sensitive data breaches are the most popular risks of credit card fraud in this modern society. Advanced deep learning algorithms have shown outstanding performance regarding fraud detection in this field. However, the proposed models still suffer from the imbalance class problem and the high rate of false positives and negatives. To overcome these challenges, we introduce in this paper a new approach based on TabNet-based feature selection and sequential hybridization of Random Forest and optimized deep learning algorithm. This model outperformed its rivals in detecting complex fraudulent patterns with confidence while minimizing false negatives. Our powerful performance is achieved by combining TabNet feature selection, outlier removal using Z -score and IQR, and strong classification of Random Forest-Deep Learning. The imbalance issue was handled using random oversampling. The results of the experiment demonstrated that our model achieved 100% accuracy, 100% precision, and 1.00 recall regardless of the dataset used which proved its generalization.