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@article{191289,
        author = {Ashish Ravindra Mewal and Dr. Syed Sumera Ali and A.G.Gaikwad and A.T. Jadhav and Dr. D.L. Bhuyar and Dr.G.B.Dongre},
        title = {A Comparative Analysis Of Synthetic Minority Over-Sampling Technique (SMOTE) In Enhancing Credit Card Fraud Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6927-6933},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191289},
        abstract = {The explosive proliferation of digital payment networks has compounded the credit card fraud threat, and it has been a big challenge to both financial institutions and consumers. The highly skewed nature of transaction data is among the greatest challenges in fraud detection since fraudulent cases are a minority within the total data. This paper will offer comparative research of ML-based credit card fraud detection models with a specific focus on how SMOTE can help deal with the issue of class imbalance. The performance of several common classifiers such as, Logistic Regression, Decision Tree, Random Forest and SVM were tested on the initial imbalanced dataset and a new SMOTE-balanced dataset was tested. Standard evaluation metrics (accuracy, precision, recall, F1-score and area under the ROC curve (AUC)) were used to evaluate model performance. The findings indicate that models that were trained with imbalanced data were both accurate but their detection rate of fraudulent transactions was low. Conversely, SMOTE application enhanced minority classes detection considerably, which resulted in considerable improvements of recall and F1-score of all classifiers.},
        keywords = {Credit card Fraud Detection, SMOTE, ML, Random Forest, Data Mining;},
        month = {January},
        }