Adaptive Hybrid Learning for Credit Card Fraud Detection: A Comparative Study of Supervised, Reinforcement, and Hybrid Models
Divya Raj Singh, Neetu Gupta · 2025
Financial fraud detection is challenged by the rarity and evolving nature of fraudulent transactions. This paper presents a comparative study of traditional classifiers (Logistic Regression, Random Forest, SGDClassifier), a reinforcement learning approach (Q-learning), and a novel hybrid model that combines Q-learning-driven feature selection with Random Forest classification. The hybrid framework applies SMOTE selectively to address class imbalance and leverages Bayesian optimization to fine-tune hyperparameters-striking a balance between computational efficiency and detection accuracy. To ensure the reliability of results, experiments were conducted on the Kaggle Credit Card Fraud Detection dataset-comprising 284,807 transactions with only$\mathbf{0. 1 7 \%}$labeled as fraudulent-and repeated across five independent runs, with average performance metrics reported. Among the traditional classification models, Random Forest demonstrated the highest precision ($0.9847 \pm$0.0109), while Logistic Regression delivered a strong balance between F1-score ($0.9093 \pm 0.0160$) and ROC-AUC ($0.9724 \pm 0.00)$. The Q-learning model, on the other hand, struggled with lower recall ($0.7686 \pm 0.0049$), likely due to challenges posed by the class imbalance. The proposed hybrid approach, which integrates Q-learning-based feature selection with Random Forest classification, achieved an F1-score of$0.8916 \pm 0.0894$and a ROCAUC of$0.95 \pm 0.07$. This reflects a relative improvement of up to 5% in F1-score over standalone Q-learning and demonstrates competitive performance when compared to conventional models. Overall, the findings highlight the hybrid model's potential as a stable and scalable solution for detecting fraud in real time.