Machine Learning Approaches for Effective Credit Card Fraud Detection: Addressing Imbalance and Enhancing Accuracy

Author(s)	Bhavitavya Isukapati, Shraddha Titare, Diksha Waghmare, Snehal Jondhale, Suhas G. Salve
Country	India
Abstract	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.
Keywords	Credit Card Fraud Detection, Machine Learning Algorithms, Data Imbalance
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-01-28
Cite This	Machine Learning Approaches for Effective Credit Card Fraud Detection: Addressing Imbalance and Enhancing Accuracy - Bhavitavya Isukapati, Shraddha Titare, Diksha Waghmare, Snehal Jondhale, Suhas G. Salve - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35045
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.35045
Short DOI	https://doi.org/g829sq

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