ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS

Banking fraud has become a pervasive challenge, necessitating innovative solutions to protect financial institutions and their customers. This study investigates the effectiveness of supervised machine learning algorithms in detecting fraudulent activities within the banking sector. We conducted a comparative analysis of five widely used algorithms: Logistic Regression, Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks. Using a real-world banking dataset, we employed robust preprocessing and fine-tuning techniques to address class imbalances and optimize model performance. The evaluation metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC), revealed that Gradient Boosting and Neural Networks consistently outperformed other models, achieving high precision and recall rates. The results highlight the potential of machine learning to detect subtle patterns of fraud while minimizing false positives and negatives. Furthermore, we discuss the implications of these findings for real-time fraud prevention systems and emphasize the importance of algorithm selection and scalability in operational environments.