An In-Depth Analysis of Convolutional Neural Networks for Fraud Detection and Prevention in Contemporary Banking

Abstract

Fraud detection and prevention have become critical challenges for the modern banking industry due to the increasing sophistication of fraudulent activities and the rapid evolution of technology. Traditional fraud detection methods often struggle to keep pace with the dynamic nature of fraud patterns, leading to substantial financial losses and reputational damage for banks. In recent years, convolutional neural networks (CNNs) have emerged as a powerful tool for fraud detection and prevention, leveraging their ability to automatically learn and extract complex patterns from large volumes of data. This comprehensive study explores the application of CNNs in fraud detection and prevention within the modern banking sector, discussing their architectures, advantages, limitations, and future prospects. By providing insights into the effective implementation and integration of CNNs in fraud management strategies, this study aims to assist banks in enhancing their fraud detection capabilities and safeguarding their assets and customers' trust in the rapidly evolving landscape of financial fraud.