AI-Enabled Real-Time Financial Fraud Detection and Encryption Impact Analysis in High-Performance Cloud-Based Enterprise Networks
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806021Keywords:
Artificial Intelligence, Real-Time Fraud Detection, Financial Cybersecurity, Big Data Analytics, Encryption Impact Analysis, High-Performance Enterprise Networks, Machine Learning, Cloud Computing, Secure Data Streaming, Network Performance OptimizationAbstract
The rapid digitization of financial services and the widespread adoption of high-performance enterprise networks have significantly increased exposure to sophisticated and large-scale financial fraud. Traditional rule-based and batch-oriented security mechanisms are inadequate for detecting evolving fraud patterns in real time, particularly in environments characterized by high data velocity, encrypted traffic, and distributed cloud infrastructures. This paper presents an AI-enabled real-time financial fraud detection framework integrated with encryption impact analysis for high-performance enterprise networks. The proposed approach leverages machine learning and deep learning models to analyze streaming financial transactions while simultaneously assessing the computational and latency overhead introduced by cryptographic mechanisms such as symmetric encryption, public-key infrastructure, and secure key management. A hybrid architecture combining edge analytics, cloud-based intelligence, and secure data pipelines is introduced to ensure scalability, low-latency detection, and regulatory compliance. Experimental analysis demonstrates that the AI-driven model achieves high fraud detection accuracy with minimal performance degradation, even under strong encryption constraints. The results highlight a balanced trade-off between security, throughput, and response time, making the proposed framework suitable for modern enterprise and financial network environments requiring both real-time intelligence and robust data protection
References
1. Agrawal, S., & Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, 708–713. https://doi.org/10.1016/j.procs.2015.08.220
2. Meshram, A. K. (2025). Real-time financial fraud prediction using big data streaming on cloud platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12834–12845.
3. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
4. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
5. Kusumba, S. (2025). Modernizing US Healthcare Financial Systems: A Unified HIGLAS Data Lakehouse for National Efficiency and Accountability. International Journal of Computing and Engineering, 7(12), 24-37.
6. Bace, R., & Mell, P. (2001). Intrusion detection systems. NIST Special Publication 800-31. National Institute of Standards and Technology.
7. Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613. https://doi.org/10.1016/j.dss.2010.08.008
8. Keezhadath, A. A., & Amarapalli, L. (2024). Ensuring Data Integrity in Pharmaceutical Quality Systems: A Risk-Based Approach. Journal of AI-Powered Medical Innovations (International online ISSN 3078-1930), 1(1), 83-104.
9. Navandar, P. (2025). AI Based Cybersecurity for Internet of Things Networks via Self-Attention Deep Learning and Metaheuristic Algorithms. International Journal of Research and Applied Innovations, 8(3), 13053-13077.
10. Panda, M. R., Mani, K., & Muthusamy, P. (2024). Hybrid Graph Neural Networks and Transformer Models for Regulatory Data Lineage in Banking. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 619-633.
11. Joseph, J. (2023). Trust, but Verify: Audit-ready logging for clinical AI. https://www.researchgate.net/profile/Jimmy-Joseph-9/publication/395305525_Trust_but_Verify_Audit-ready_logging_for_clinical_AI/links/68bbc5046f87c42f3b9011db/Trust-but-Verify-Audit-ready-logging-for-clinical-AI.pdf
12. Diffie, W., & Hellman, M. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654. https://doi.org/10.1109/TIT.1976.1055638
13. Vemula, H. L., Khatri, S., Vijayalakshmi, D., & Hatole, S. (2025). Artificial Intelligence in Consumer Decision-Making: A Review of AI-Driven Personalization and Its Managerial Implications. Journal of Informatics Education and Research, 5(2). https://doi.org/10.52783/jier.v5i2.2631
14. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY-PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 3(4), 3400-3405.
15. Sugumar, R. (2024). Quantum-Resilient Cryptographic Protocols for the Next-Generation Financial Cybersecurity Landscape. International Journal of Humanities and Information Technology, 6(02), 89-105.
16. Natta, P. K. (2025). Architecting autonomous enterprise platforms for scalable, self-regulating digital systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(5), 17292–17302. https://doi.org/10.15662/IJAESIT.2025.0805002
17. Gangina, P. (2025). Demystifying Zero-Trust Architecture for Cloud Applications. Journal of Computer Science and Technology Studies, 7(9), 542-548.
18. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
19. Sriramoju, S. (2023). Optimizing customer and order automation in enterprise systems using event-driven design. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9006–9016.
20. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
21. Genne, S. (2025). Bridging the Digital Divide: Mobile Web Engineering as a Pathway to Equitable Higher Education Access. Journal of Computer Science and Technology Studies, 7(7), 560-566.
22. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
23. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
24. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348-1353). IEEE.
25. Sharma, A., & Joshi, P. (2024). Artificial Intelligence Enabled Predictive Decision Systems for Supply Chain Resilience and Optimization. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 7460–7472. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4715
26. Fernandes, D. A. B., Soares, L. F. B., Gomes, J. V., Freire, M. M., & Inácio, P. R. M. (2014). Security issues in cloud environments: A survey. International Journal of Information Security, 13(2), 113–170. https://doi.org/10.1007/s10207-013-0208-7
