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@article{184692,
        author = {Sivakumar Karuppiah},
        title = {Detecting Credit Card Fraud Using Advanced Machine Learning Models in Banking Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3109-3116},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184692},
        abstract = {Credit card fraud continues to be a major threat to the financial ecosystem, costing billions annually and undermining consumer trust. In response, banks and fintech firms are increasingly leveraging advanced machine learning (ML) models to detect fraudulent transactions in real time. This review presents a comprehensive examination of the evolution, implementation, and efficacy of ML algorithms in credit card fraud detection over the past decade. It highlights the transition from rule-based systems to ensemble and deep learning models such as XGBoost, CNN, LSTM, and hybrid CNN-LSTM frameworks. Through comparative experiments and theoretical modeling, we assess the performance, scalability, and limitations of these techniques. The study also explores critical areas such as model interpretability, privacy-preserving learning (e.g., federated learning), and adversarial robustness. Concluding with forward-looking perspectives, this review offers a roadmap for the future development of resilient, transparent, and adaptive fraud detection systems tailored to the needs of modern banking environments.},
        keywords = {Credit Card Fraud Detection; Machine Learning; Deep Learning; XGBoost; CNN-LSTM; Federated Learning; Adversarial Robustness; Explainable AI; Banking Systems; Real-Time Fraud Detection},
        month = {September},
        }