Credit Card Fraud Detection Using Bootstrap Aggregation and Random Forest

The fast increase of digital transactions and the changing type of fraudulent activity make credit card fraud detection still a great difficulty for financial institutions. Mostly rule-based systems, traditional fraud detection techniques can find it difficult to handle the class imbalance in transaction datasets, when fraudulent cases make up a negligible fraction. This work investigates how to solve these problems using advanced machine learning methods, especially ensemble approaches. We specifically assess the efficiency of Random Forest and Bootstrap Aggregation (Bagging) classifiers. There are 284,807 transaction records in the dataset utilized for this study; just 492 of these have labels as fraudulent. Random up-sampling of the minority class and feature normalizing techniques were used in data preprocessing to balance the dataset and improve model performance. Trained on an 80% training set, both classifiers were assessed using measures including precision, recall, accuracy, and confusion matrices a 20% test set. The Random Forest classifier somewhat outperformed the Bagging classifier in terms of total accuracy and misclassification rates, the findings show that both models attained near-perfect precision and recall. With 100% recall, both models found all false transactions without missing any. These findings highlight the possibilities of ensemble learning methods in creating very dependable fraud detection systems able to provide real-time, scalable, and interpretable performance in operational environments. This study helps to identify fraudulent transactions, so guaranteeing better safety for customers and financial institutions.