Deep Learning for Anomaly Detection in E-commerce and Financial Transactions: Enhancing Fraud Prevention and Cybersecurity

Abstract

E-commerce and financial transaction platforms are increasingly vulnerable to cyber threats and fraudulent activities due to the rapid digitization of global markets. Anomaly detection plays a vital role in identifying unusual behavior indicative of fraud, security breaches, or financial manipulation. Traditional methods such as rule-based systems and statistical models often fall short in adapting to evolving patterns of fraud. Deep learning, with its ability to extract complex features and learn non-linear relationships from massive datasets, offers a transformative approach to anomaly detection. This paper explores the use of deep learning techniques—such as Autoencoders, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs)—to enhance fraud prevention and cybersecurity in the e-commerce and financial sectors. The paper highlights the comparative effectiveness of different models, challenges such as data imbalance and explainability, and future prospects for integrated intelligent systems.