Efficient and Robust Fraud Detection in Banking: A Machine Learning Perspective

Abstract

Anomaly detection is a key technique in identifying fraud in various domains such as finance, healthcare, and e-commerce. Fraudulent activities often represent anomalous behavior within a system, making anomaly detection essential for uncovering fraud that would otherwise go unnoticed. This paper presents a comprehensive overview of the various anomaly detection techniques applied to fraud detection. It covers traditional methods such as statistical approaches, machine learning algorithms, and modern advancements in deep learning. Furthermore, we discuss the challenges associated with these techniques, such as false positives, scalability, and adaptability, and provide insights into future trends in fraud detection.