Leveraging Machine Learning for Enhanced Bank Fraud Detection and Prevention

Abstract

The increasing digitization of banking services has brought both convenience and a rise in fraudulent activities. Traditional rule-based fraud detection methods often fail to keep up with sophisticated fraud techniques, leading to significant financial losses. Machine learning (ML) provides a more dynamic and efficient approach to detecting and preventing fraud in the banking sector. This research paper explores the role of machine learning in enhancing fraud detection systems, discusses various machine learning algorithms, examines their advantages and limitations, and highlights the future potential of ML in fraud prevention. This paper proposes an integrated ML-based fraud detection framework, combining supervised, unsupervised, and hybrid techniques, and demonstrates how they can significantly improve the detection of fraudulent activities in real-time.