Credit Card Fraud Detection Using Optimized Machine Learning Models

Abstract

The rapid growth of online banking and digital payment systems, credit card fraud has become a critical challenge for financial institutions, resulting in significant financial losses and reduced customer trust. This study focuses on the detection and prevention of fraudulent credit card transactions using advanced machine learning techniques. The primary objective of this work is to analyze existing fraud detection approaches and address key challenges such as data imbalance, feature engineering, feature selection and model optimization. The dataset used in this study consists of transactional records containing both legitimate and fraudulent activities with fraud cases representing a highly imbalanced minority class. Various data preprocessing techniques are applied, including missing value handling, normalization and categorical encoding. Several machines learning classifiers, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost and Neural Networks are trained and evaluated. Hyperparameter tuning using Grid search and Randomized Search is performed to improve the robustness and the predictive accuracy. Experimental results demonstrate a significant improvement in fraud detection performance, achieving high accuracy, precision, recall and Area under the curve (AUC) scores. Ensemble-based models, particularly Random Forest and Gradient boosting, emerge as the most effective in identifying fraudulent transactions. The findings confirm that proper feature engineering, imbalance handling and model optimization play a vital role in building reliable and efficient credit card fraud detection systems.