Financial Fraud Detection in E-commerce Transactions using DL
Keywords:
Financial Fraud, E-commerce, Deep Learning, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Data Imbalance, Explainable AI (XAI)Abstract
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.
Downloads
References
Abdul Salam, M., Fouad, K., Elbably, D., et al. (2024). Federated learning model for credit card fraud detection with data balancing techniques. Neural Computing & Applications, 36(1), 44.
Alief, R. N., Rachmawati, S. M., Lee, J. M., et al. (2023). Malicious account classification using cnn for ethereum blockchain’s accounts. Journal of the Korean Institute of Communications and Information Sciences, 48(7), 875–884.
Brughmans, D., Leyman, P., Martens, D. (2024). Nice: an algorithm for nearest instance counterfactual explanations. Data Mining and Knowledge Discovery, 38, 2665–2703.
Campanella, F., Serino, L., Battisti, E., Giakoumelou, A., et al. (2023). Fintech in the financial system: Towards a capital-intensive and high competence human capital reality? Journal of Business Research, 155, 113376.
Cao, L. (2022). Ai in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(1), 1–38.
CerneviCiene, J., & Kabasinskas, A. (2024). Explainable artificial intelligence (xai) in finance: A systematic literature review. Artificial Intelligence Review, 57, 216.
Cheng, D., Ye, Y., Xiang, S., et al. (2023). Anti-money laundering by group-aware deep graph learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12444–12457.
Commission, F. T. (2024). Consumer sentinel network: Data book 2023. Federal Trade Commission.
Fernandez, J., Chiachio, M., Chiachio, J., et al. (2022). Uncertainty quantification in neural networks by approximate bayesian computation. Engineering Applications of Artificial Intelligence, 107, 104511.
Haixiang, G., Yijing, L., Shang, J., et al. (2017). Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications, 73, 220–239.
Jurgovsky, J., Granitzer, M., Ziegler, K., et al. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
Lokanan, M. E., & Maddhesia, V. (2025). Supply chain fraud prediction with machine learning and artificial intelligence. International Journal of Production Research, 63(1), 286–313.
Miao, Z. (2024). Financial fraud detection and prevention: Automated approach based on deep learning. Journal of Organizational and End User Computing, 36(1), 27.
Ming, R., Abdelrahman, O., Innab, N., et al. (2024). Enhancing fraud detection in auto insurance and credit card transactions: A novel approach integrating cnns and machine learning algorithms. PeerJ Computer Science, 10, e1689.
Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, 122156.
Ngai, E. W. T., Hu, Y., Wong, Y. H., et al. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50, 559–569.
Ozbayoglu, A. M., Gudelek, M. U., Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384.
Thakur, A., & Arya, S. (2024). Comparative analysis of manual and programmed annotations for crowd assessment and classification using artificial intelligence. Data Science and Management, 7, 340–348.
Vouk, B., Guid, M., Robnik-Sikonja, M. (2023). Feature construction using explanations of individual predictions. Engineering Applications of Artificial Intelligence, 120, 105823.
Wang, G., Ma, J., Chen, G. (2023a). Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention. Decision Support Systems, 167, 113913.
Zioviris, G., Kolomvatsos, K., Stamoulis, G. (2024). An intelligent sequential fraud detection model based on deep learning. The Journal of Supercomputing, 80, 14824–14847.
Zhu, H., Vigren, O., Soderberg, I. L., et al. (2024a). Implementing artificial intelligence empowered financial advisory services: A literature review and critical research agenda. Journal of Business Research, 174, 114494.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
