Machine Learning Approaches for Real-Time Bank Fraud Detection:

Abstract

The rapid advancement in financial technology has led to an increase in online banking and electronic transactions, making fraud detection a critical issue for the banking sector. Traditional fraud detection systems often rely on rule-based approaches, which are insufficient to handle the complexity and volume of real-time transactions. Machine learning (ML) offers powerful solutions to enhance the detection of fraudulent activities by identifying hidden patterns, anomalies, and trends within transaction data.  Key algorithms such as decision trees, support vector machines, neural networks, and ensemble methods are examined, with a focus on their application to large-scale and dynamic datasets. The challenges of implementing these methods in real-time environments, including data imbalance, interpretability, and computational efficiency, are also addressed. Furthermore, emerging trends such as the integration of deep learning and anomaly detection for enhanced accuracy are highlighted. The study concludes by emphasizing the importance of continuous improvement in ML models to adapt to evolving fraud tactics and maintain robust, real-time fraud detection systems.