Fraud Detection in Card Transactions via Random Forest

Credit card scam has become a growing concern in today’s digital economy, where millions of transactions take place online every day. As fraudsters continue to find new ways to exploit vulnerabilities, traditional rule-based detection systems struggle to keep up. This research focuses on developing a machine learning-based solution using the Random Forest algorithm to accurately identify and prevent fraudulent credit card transactions. The proposed system is trained and evaluated on a real-world dataset containing one million transaction records. Each record includes key features such as transaction amount, distance from the cardholder’s home and last transaction, chip usage, online order status, and other behavior-based indicators. RF, a powerful ensemble learning method, was selected for its ability to handle large datasets, manage class imbalance, and provide high accuracy by combining multiple decision trees. The model is trained to detect subtle patterns in user behavior and flag anomalies that might indicate fraudulent activity. Various preprocessing steps, such as normalization and feature selection, were applied to enhance model performance.Theoutcomes show that the Random Forest algorithm achieves strong performance in detecting fraud, even when fraudulent transactions are a small fraction of the dataset. Precision, recall, and F1-score metrics confirm the model’s robustness. This study not only demonstrates the effectiveness of machine learning in fraud detection but also highlights the importance of using behavioral transaction data to improve prediction accuracy. The findings can aid financial institutions in implementing smarter, data-driven fraud prevention systems with real-time alerting capabilities.