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@article{194848,
        author = {M Rama Krishna Raju and P Hemanth Kumar Varma and T Swathi and S Kranthi Durga and V Surendra Raju},
        title = {Credit Card Fraud Detection Using Machine Learning Classification Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8036-8040},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194848},
        abstract = {The rapid expansion of digital payment ecosystems has significantly increased the volume of credit card transactions worldwide. While electronic payments offer convenience and efficiency, they also expose financial institutions to fraudulent activities that result in substantial economic losses. Traditional rule-based fraud detection systems struggle to detect evolving fraud patterns and suffer from high false-positive rates. This research proposes a machine learning-based classification framework for detecting fraudulent credit card transactions. The system incorporates data preprocessing, feature scaling, class imbalance handling using Synthetic Minority Oversampling Technique (SMOTE), and multiple supervised learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and XGBoost. Performance is evaluated using precision, recall, F1-score, confusion matrix, and ROC-AUC metrics. Experimental results demonstrate that ensemble-based models achieve superior fraud detection capability with improved recall and reduced false alarms. The proposed solution provides a scalable, adaptive, and efficient fraud detection mechanism suitable for modern banking environments.},
        keywords = {Credit Card Fraud, Machine Learning, Classification, SMOTE, Random Forest, XGBoost, Imbalanced Data, ROC-AUC},
        month = {March},
        }