Detecting Fraudulent Credit Card Transactions using Ensemble Methods

Detecting fraudulent credit card transactions is a significant challenge for financial institutions, as cybercriminals often impersonate legitimate users to conduct unauthorized transactions. Since every approved transaction requires verification, effective fraud detection mechanisms are essential. Ensemble learning techniques can enhance fraud detection by analyzing both synthetic and real-world consumer transaction datasets. Models such as XGBoost, random forests, and naive Bayes classifiers are tested, with performance evaluated based on accuracy, precision, recall, and F1 score. Results show that ensemble classifiers perform well on real transaction data but struggle with synthetic datasets. Real-world transaction systems adapt quickly to structured patterns, improving fraud detection effectiveness. However, the deterministic nature of these systems, combined with minimal randomness, may increase the vulnerability of credit card information.