A Survey on Financial Fraud Detection in E-commerce Transactions using Deep Learning

The rapid expansion of e-commerce platforms has revolutionized global trade and consumer behavior, offering unprecedented convenience and accessibility. However, this digital transformation has concurrently catalyzed a parallel surge in sophisticated financial fraud attempts, resulting in substantial economic losses amounting to billions of dollars annually. Traditional rule-based fraud detection systems and conventional machine learning approaches increasingly demonstrate inadequacy when confronted with the evolving complexity, scale, and adaptive nature of modern fraudulent activities. This survey paper provides a comprehensive examination of deep learning methodologies applied to financial fraud detection within e-commerce transactions. It systematically reviews the landscape of fraud types, evaluates the efficacy of various deep learning architectures including Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Autoencoders, and hybrid ensemble models. The paper identifies critical research gaps pertaining to imbalanced datasets, concept drift, explainability requirements, and real-time processing constraints. Through synthesis of contemporary literature from 2020 onwards, this survey proposes a methodological framework that addresses identified limitations while highlighting promising directions for future investigation. The findings underscore the transformative potential of deep learning in augmenting fraud detection capabilities, whilst acknowledging persistent challenges that necessitate interdisciplinary collaboration between data scientists, financial institutions, and regulatory bodies.