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@article{192250,
        author = {Mr. Kalpak Ramidwar and Mr. Chetan Bhankhede and Mr. Sumedh Khonde and Miss. Shravani Suddalwar and Miss. Latika Dagwar and Miss. Samrudhi Tatewar},
        title = {Credit Card Fraud Detection Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {759-764},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192250},
        abstract = {Credit card fraud has emerged as a major problem because of the fast development of online and digital payment systems. It is difficult to identify fraudulent transactions efficiently because of the highly imbalanced nature of the transaction data and the ever-changing patterns of credit card fraud. This paper proposes a machine learning-based system for credit card fraud detection using classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM). The proposed system examines the characteristics of transactions to detect anomalies in real-time. The experimental results demonstrate that ensemble learning methods, particularly Random Forest, perform better than traditional approaches in terms of accuracy and fraud detection rates. The proposed system can help banks prevent financial losses and ensure transaction security.},
        keywords = {Credit Card Fraud Detection, Machine Learning, Random Forest, Classification, Imbalanced Dataset, Financial Security, Transaction Data Analysis, Fraud Prevention},
        month = {February},
        }