Optimizing Bank Fraud Detection Systems Using Advanced Machine Learning Models

Abstract

The increasing sophistication of financial fraud presents a significant challenge for banks and financial institutions, necessitating advanced detection systems to safeguard against fraudulent activities. This paper explores the optimization of bank fraud detection systems through the application of cutting-edge machine learning models. We begin by analyzing current fraud detection methodologies and identifying their limitations in the context of evolving fraud tactics. The core of this study involves the implementation and comparative evaluation of several advanced machine learning techniques, including ensemble learning, deep neural networks, and anomaly detection algorithms. Through extensive experimentation with real-world datasets, we assess the performance of these models in terms of accuracy, precision, recall, and computational efficiency. Our findings demonstrate that integrating advanced machine learning approaches significantly enhances the ability to detect and mitigate fraudulent transactions while reducing false positives. The study provides actionable insights into model selection, parameter tuning, and system integration, offering a comprehensive framework for optimizing fraud detection systems in the banking sector. The implications of these advancements are discussed, highlighting their potential to improve security and operational efficiency in financial institutions.