Credit Card Fraud Detection Using Machine Learning Techniques

Abstract

Credit card fraud is a serious issue in the financial sector and can cause financial losses for customers, banks, and payment service providers. As digital transactions continue to grow, it has become increasingly important to detect fraudulent activity quickly and accurately. Traditional fraud detection systems often rely on predefined rules, which can make it difficult to recognize new or changing patterns of fraud. Machine learning offers another approach by learning patterns from previous transaction data and using those patterns to classify new transactions as legitimate or fraudulent. This research compares four machine learning techniques for credit card fraud detection: Logistic Regression, Random Forest, Support Vector Machine, and XGBoost. The proposed process includes data preprocessing, handling class imbalance, preparing features, training the models, and evaluating their performance. Accuracy, precision, recall, F1-score, and ROC-AUC are considered as evaluation measures. Particular attention is given to precision and recall because fraud datasets are usually highly imbalanced, with fraudulent transactions making up only a small part of all transactions. Overall, simple models such as Logistic Regression can provide an understandable baseline, while ensemble methods such as Random Forest and XGBoost can capture more complicated transaction patterns. The study shows that machine learning can be useful for fraud detection when it is combined with suitable preprocessing, class-imbalance techniques, and careful evaluation.