Intelligent Payment Fraud Detection: Applying Deep Learning Models to Secure Financial Transactions
Abstract
The rapid growth of digital financial transactions has led to a surge in payment fraud, necessitating advanced detection mechanisms that go beyond traditional rule-based systems. This paper presents an intelligent fraud detection framework that leverages deep learning models to enhance the security of financial transactions. Utilizing architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, the system captures complex patterns, temporal dependencies, and non-linear relationships within transactional data. The model is trained on large-scale, anonymized datasets containing both legitimate and fraudulent transactions, achieving high accuracy and a low false positive rate. Additionally, the framework supports real-time processing to ensure timely detection and response. Experimental results demonstrate the superiority of deep learning approaches over traditional machine learning algorithms in detecting both known and previously unseen fraud patterns. The proposed solution offers a scalable and adaptive defense against evolving threats in the financial ecosystem.
References
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
Chen, C., & Chen, M. (2020). A hybrid deep learning model for detecting financial fraud. In Proceedings of the IEEE Symposium on Computers and Communications (pp. 345–350).
Whitrow, C., Hand, D. J., Juszczak, P., Weston, D., & Adams, N. M. (2009). Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1), 30–55.
Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017). GOTCHA! Network-based fraud detection for social security fraud. Management Science, 63(9), 3090–3110.
Pozzolo, A. D., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3784–3797.
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y. A., Caelen, O., Mazzer, Y., & Bontempi, G. (2019). Scarff: A scalable framework for streaming credit card fraud detection with Spark. Information Fusion, 41, 182–194.
Roy, A., Sun, J., Mahoney, W., Alshboul, R., & Prabakar, N. (2018). Deep learning detecting fraud in credit card transactions. In Proceedings of the IEEE International Conference on Big Data (pp. 1846–1855).
Kim, Y., & Kim, D. (2021). Financial fraud detection using machine learning and deep learning algorithms. Journal of Intelligent & Fuzzy Systems, 40(5), 9175–9185.
Cardozo, K., Nehmer, L., Esmat, Z. A. R. E., Afsari, M., Jain, J., Parpelli, V., ... & Shahid, T. (2024). U.S. Patent No. 11,893,819. Washington, DC: U.S. Patent and Trademark Office.
Gupta, M., & Jain, J. (2024). Optimizing E-commerce Dynamic Pricing Using Aggregated Market Data and Cloud-Based Analytics. International Journal of Global Innovations and Solutions (IJGIS).
Jain, J., & Gupta, M. (2024). Enhancing Software Engineering Practices for AI-Driven Fintech Applications. International Journal of Global Innovations and Solutions (IJGIS).
Jain, J. (2024). AI-Driven Optical Character Recognition for Fraud Detection in FinTech Income Verification Systems.
Cardozo, Kenneth, Landon Nehmer, Z. A. R. E. Esmat, Mani Afsari, Jitender Jain, Venkateshwar Parpelli, Bhuvaneswari Balasubramanian, Bijun
Du, Daniel Nizinski, and Tausif Shahid. "Systems and methods for extracting and processing data using optical character recognition in real-time environments." U.S. Patent Application 18/429,247, filed May 23, 2024.
Jain, J. Leveraging Advanced AI and Cloud Computing for Scalable Innovations in Fintech Systems, 2022.
Jain, J., Khunger, A., Agarwal, G., Tanikonda, A., & Modake, R. (2021). Optimizing Payment Gateways in Fintech Using AI-Augmented OCR and Intelligent Workflow. Authorea Preprints.
Jain, J., Modake, R., Khunger, A., & dnyandev Jagdale, A. CLOUD-NATIVE SECURITY FRAMEWORK: USING MACHINE LEARNING TO IMPLEMENT SELECTIVE MFA IN MODERN BANKING PLATFORMS , 2019.
Bahnsen, A. C., Aouada, D., Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card fraud detection. In Proceedings of the 2016 International Conference on Machine Learning and Applications (pp. 1129–1135).
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.
