Credit Card Fraud Detection using Machine Learning and Behavioral Pattern Analysis

Credit card fraud has become a serious challenge for financial institutions due to the rapid growth of online transactions and highly imbalanced transaction data. Traditional fraud detection methods often struggle to adapt to evolving fraud patterns and minority-class dominance. In this work, a publicly available Kaggle credit card transaction dataset containing 284,807 records is used, where fraudulent transactions represent a very small proportion of the data. The dataset includes time-based features, transaction amount, and anonymized principal components generated using PCA. To address class imbalance and improve learning efficiency, data preprocessing techniques such as feature scaling, feature selection, and SMOTE-ENN balancing are applied. Multiple machine learning and deep learning models, including Random Forest, Decision Tree, K-Nearest Neighbors, LSTM, GRU, and MLP, are evaluated individually and in ensemble form. A Voting Classifier combining complementary models is proposed to improve robustness and detection accuracy. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that the ensemble approach achieves superior performance compared to individual models. The study demonstrates that combining behavioral patterns with ensemble learning can significantly enhance fraud detection effectiveness while maintaining practical applicability.