Next-Generation Fraud Detection in Banking: A Machine Learning Approach

Abstract

Fraud detection in banking has become increasingly complex with the rise of sophisticated fraudulent schemes and the exponential growth of transaction data. Traditional rule-based systems are often insufficient to handle the volume and complexity of modern financial transactions. This research explores the application of machine learning (ML) techniques to enhance fraud detection systems. By leveraging various ML algorithms, including supervised and unsupervised learning, this study aims to improve detection accuracy, reduce false positives, and adapt to evolving fraudulent patterns. The research evaluates several ML models, including decision trees, neural networks, and clustering algorithms, assessing their performance in real-world banking scenarios. The results indicate that ML approaches, particularly ensemble methods and deep learning, significantly outperform traditional methods in terms of accuracy and adaptability. This paper provides a comprehensive overview of these techniques and discusses their potential to transform fraud detection practices in the banking industry.