A Predictive Model for Credit Card Scam Detection Using Random Forest

Author(s)	Dr. Vijay Kumar Samyal, Mr. Sudhanshu Kumar
Country	India
Abstract	Credit card fraud has become a major concern in the digital economy, where the rapid growth of online transactions increases the risk of unauthorized financial activities. Effective fraud detection systems are essential to safeguard consumers and financial institutions from significant economic losses. This study investigates the use of machine learning models, specifically Logistic Regression and Random Forest, to classify credit card transactions as legitimate or fraudulent.The dataset is preprocessed through feature scaling and class-imbalance handling techniques such as SMOTE to improve model performance. Experimental evaluation shows that the models achieve high accuracy, with Random Forest performing comparatively better due to its ability to capture complex patterns in transactional data. The results demonstrate the potential of machine learning–based approaches in enhancing the reliability and speed of fraud detection, enabling timely intervention and improved financial security.
Keywords	Credit Card Fraud Detection, Machine Learning, Random Forest, SMOTE, Anomaly Detection, Financial Security.
Field	Engineering
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-12-28
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9906
Short DOI	https://doi.org/hbhj6f

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