Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

AI-Augmented Fraud Detection and Cybersecurity Framework for Digital Payments and E-Commerce Platforms

Haranadha Reddy Busireddy Seshakagari
Manager - Architecture, Valuemomentum, Erie, PA-16506, USA.
Deventhira HariramNathan
Director - Delivery, Valuemomentum, Erie, PA-16506, USA.

Published 2025-06-09

Keywords

  • AI in Cybersecurity,
  • Fraud Detection,
  • Digital Payments,
  • Anomaly Detection,
  • Behavioral Analytics,
  • Deep Learning
  • ...More
    Less

How to Cite

Haranadha Reddy Busireddy Seshakagari, & Deventhira HariramNathan. (2025). AI-Augmented Fraud Detection and Cybersecurity Framework for Digital Payments and E-Commerce Platforms. International Journal of Computational Learning & Intelligence, 4(4), 832–846. https://doi.org/10.5281/zenodo.15624056

Abstract

Advanced fraud detection techniques are becoming more and more necessary as e-commerce and digital transactions continue to grow.  The intricacy and changing nature of fraudulent actions can make it difficult for traditional rule-based systems to keep up. To tackle this challenge, this research presents a hybrid fraud detection framework that combines several machine learning techniques, including Logistic Regression, XGBoost, a fusion of Autoencoder with XGBoost, and Graph Neural Networks (GNN). The proposed system also integrates behavioral pattern analysis and real-time risk evaluation, enabling it to adapt swiftly to new threats. Comprehensive testing on both standard and real-world datasets demonstrates the strength of this approach. The Autoencoder-XGBoost combination emerged as the top performer, achieving 97.4% accuracy with precision, recall, and F1-score, all at 0.96, and operating with a latency of just 100 milliseconds. The GNN model also delivered strong results, reaching 96.7% accuracy, a precision of 0.95, a recall of 0.94, and an F1-score of 0.945 while maintaining a lower latency of 88 milliseconds. Comparatively, traditional models like Logistic Regression and standalone XGBoost achieved 89.5% and 94.2% accuracy, respectively. These results highlight the improved effectiveness of hybrid approaches in identifying fraud within modern digital ecosystems.

References

  1. Patel, P., & Kaur, J. (2025). Introduction to brand management in the digital age. In Strategic brand management in the age of AI and disruption (pp. 1–26). IGI Global Scientific Publishing.
  2. Nabila, S., & Fasa, M. I. (2025). Digital transformation and Generation Z’s interest in Islamic banking products: Evidence from Lampung Province. DEAL: International Journal of Economics and Business, 3(01), 56–61.
  3. Ndibe, O. S. (2025). AI-driven forensic systems for real-time anomaly detection and threat mitigation in cybersecurity infrastructures. [Manuscript in preparation or unpublished work].
  4. Afzal, M., Meraj, M., Kaur, M., & Ansari, M. S. (2025). How does cybersecurity awareness help in achieving digital financial inclusion in rural India under escalating cyber fraud scenario? Journal of Cyber Security Technology, 9(2), 88–126.
  5. Al Obaidi, B. S. H., Al Kareem, R. S., Kadhim, A. T., & Korchova, H. (2025). The ripple effects of fraud on businesses: Costs, reputational damage, and legal consequences. Encuentros: Revista de Ciencias Humanas, Teoría Social y Pensamiento Crítico(23), 345–371.
  6. Tarade, R., & Das, S. (2025). Cybersecurity in the age of AI—Enhancing defences for today’s threats. In Critical phishing defense strategies and digital asset protection (p. 309).
  7. Singh, N., Jain, N., & Jain, S. (2025). AI and IoT in digital payments: Enhancing security and efficiency with smart devices and intelligent fraud detection. International Research Journal of Modernization in Engineering Technology and Science, 6(12), 982–991.
  8. Pathak, D. N., Kumar, A., Srivastava, K., Ranjan, R., Kaur, K., & Singh, R. (2025). Improving e-commerce fraud detection: A GAN and reinforcement learning approach integrated with personality analysis for secure digital economy. In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV) (pp. 201–206). IEEE.
  9. Bolla, R. L., Ayyadurai, R., Parthasarathy, K., Panga, N. K. R., Bobba, J., & Ogundokun, R. O. (2025). Cloud and IoT data-based real-time fraud detection in e-commerce transactions using FSMNN approach. Service Oriented Computing and Applications, 1–17.
  10. Gopalsamy, M. (2025). Enhancing financial security based on machine learning techniques for anomaly detection in fraud transactions. [Manuscript in preparation or unpublished work].
  11. Islam, M. M., Zerine, I., Rahman, M. A., Islam, M. S., & Ahmed, M. Y. (2024). AI-driven fraud detection in financial transactions: Using machine learning and deep learning to detect anomalies and fraudulent activities in banking and e-commerce transactions. [Manuscript in preparation or unpublished work].
  12. Mahesar, A. J., Wighio, A. A., Imtiaz, N., Jamali, A., Nawaz, Y., & Urooj, U. (2025). Predicting tax evasion using machine learning: A study of e-commerce transactions. Spectrum of Engineering Sciences, 3(4), 840–852.
  13. Mahveen, Z. (2025). Optimizing fraud detection in healthcare: A hybrid machine learning approach. [Manuscript in preparation or unpublished work].
  14. Mienye, I. D., & Swart, T. G. (2025). Deep autoencoder neural networks: A comprehensive review and new perspectives. Archives of Computational Methods in Engineering, 1–20.
  15. Leveni, F., Cassales, G. W., Pfahringer, B., Bifet, A., & Boracchi, G. (2025). Online isolation forest. arXiv Preprint arXiv:2505.09593. https://arxiv.org/abs/2505.09593
  16. Ahmed, S. T., Fathima, A. S., Nishabai, M., & Sophia, S. (2024). Medical ChatBot assistance for primary clinical guidance using machine learning techniques. Procedia Computer Science, 233, 279-287.
  17. Ahmed, S. T., Priyanka, H. K., Attar, S., & Patted, A. (2017, June). Cataract density ratio analysis under color image processing approach. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 178-180). IEEE.
  18. Sreedhar Kumar, S., Ahmed, S. T., Mercy Flora, P., Hemanth, L. S., Aishwarya, J., GopalNaik, R., & Fathima, A. (2021, January). An Improved Approach of Unstructured Text Document Classification Using Predetermined Text Model and Probability Technique. In ICASISET 2020: Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India (p. 378). European Alliance for Innovation.
  19. Raja, D. K., Kumar, G. H., Basha, S. M., & Ahmed, S. T. (2022). Recommendations based on integrated matrix time decomposition and clustering optimization. International Journal of Performability Engineering, 18(4), 298.