Financial Fraud Detection in E-commerce Transactions using DL

The rapid expansion of global e-commerce has fundamentally altered the consumer landscape, but it has simultaneously introduced unprecedented opportunities for sophisticated financial misconduct. As digital transaction volumes soar into the billions annually, traditional fraud detection systems based on rigid rule-sets and manual audits have proven increasingly inadequate, suffering from high false-positive rates and an inability to adapt to evolving criminal tactics. This research paper presents a comprehensive study on the application of deep learning (DL) architectures to identify and mitigate fraudulent activities in e-commerce environments. Drawing on a systematic analysis of recent advancements, this study examines the efficacy of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models in capturing high-dimensional, non-linear fraud signals. Furthermore, this paper addresses the critical challenges of data imbalance, where fraudulent transactions constitute a minute fraction of total data, and the legal necessity for explainability in automated decision-making. By proposing an integrated methodology that aligns technical innovation with regulatory requirements, this research aims to provide a scalable and robust framework for strengthening financial integrity in increasingly complex digital ecosystems.