Machine Learning Approaches for Effective Credit Card Fraud Detection: Addressing Imbalance and Enhancing Accuracy
Bhavitavya Isukapati -, Shraddha Titare -, Diksha Waghmare, S. S. Jondhale, Suhas G. Salve - · International Journal For Multidisciplinary Research · 2025
Credit card fraud has emerged as a significant threat to the financial sector, driven by the rapid growth in online transactions and the evolving sophistication of fraudulent activities. This research aims to design and implement a machine learning-based solution capable of detecting fraudulent credit card transactions effectively. By addressing challenges such as dataset imbalance and false positives, the research employs preprocessing techniques including Synthetic Minority Oversampling Technique (SMOTE), along with advanced machine learning algorithms like Logistic Regression, XGBoost, and Isolation Forest. It highlights the potential of these models to enhance fraud detection accuracy and scalability, providing a practical and deployable tool for real-world applications.