Revolutionizing Fraud Detection in Banking Using Machine Learning Techniques

Abstract

In the rapidly evolving financial landscape, fraud detection remains a critical issue for banks worldwide. Traditional methods of fraud detection have become insufficient to address the sophisticated schemes employed by fraudsters. Machine learning (ML) techniques present a transformative approach to tackling fraud, offering dynamic, adaptive, and efficient detection methods. This paper explores the application of machine learning in revolutionizing fraud detection within the banking sector, focusing on the benefits, challenges, and future potential of ML-based solutions.