ADVANCED CREDIT CARD FRAUD DETECTION USING ENSEMBLE LEARNING ALGORITHMS

Abstract

The objective of this research is to improve the reliability and accuracy of financial system fraud detection through the use of advanced credit card fraud detection algorithms powered by ensemble learning algorithms. Credit card fraud has emerged as a significant concern for banks and other financial organizations due to the rapid expansion of online banking and digital payments. In order to more accurately evaluate transaction patterns and identify suspicious activity, the proposed paper investigates the use of ensemble methodologies, such as Random Forest, Gradient Boosting, AdaBoost, and XGBoost, in contrast to traditional machine learning models. Ensemble learning enhances the accuracy of predictions, reduces the number of false positives, and enhances the system's resilience to imbalanced datasets, all of which are common issues in fraud detection. In order to facilitate successful fraud detection, the paper prioritizes data preparation, feature selection, and model evaluation measures such as accuracy, precision, recall, and F1-score. The ultimate objective is to develop a fraud detection system that is both intelligent and scalable, thereby enabling institutions to mitigate losses and enhance the security of transactions in real time.