Credit Card Fraud Detection Using Machine Learning

The rapid growth of digital transactions has led to a significant increase in credit card fraud, re- sulting in substantial financial losses for individuals, businesses, and financial institutions worldwide. Detecting fraudulent transactions in real-time is a critical challenge due to the imbalanced nature of fraud datasets and the evolving tactics of fraudsters. This paper presents a robust machine learning- based approach to credit card fraud detection using the Random Forest algorithm. The proposed system leverages a dataset of transaction features, including time, amount, and 28 anonymized variables. The Random Forest algorithm is chosen for its ability to handle imbalanced datasets, resist overfitting, and provide interpretable results through feature importance analysis. The system achieves an accuracy of 99.93%, with precision and recall rates of 99.78% and 99.85%, respectively, demonstrating its effec- tiveness in identifying fraudulent activities. The study also addresses challenges such as scalability and computational efficiency, making the system suitable for real-time applications. By automating fraud detection, this solution aims to reduce financial losses, enhance transaction security, and build trust in digital payment systems. Future work will focus on integrating advanced preprocessing techniques, optimizing the model for real-time deployment, and exploring ensemble methods to further improve performance.