A Unified AI and Cloud Security Model for Financial Fraud Prevention and Medical Image Intelligence in 5G-Powered Web Applications

Aoife Marie Gallagher

doi:10.15662/IJRAI.2022.0504006

Authors

Aoife Marie Gallagher Senior Cybersecurity Engineer, Ireland Author

DOI:

https://doi.org/10.15662/IJRAI.2022.0504006

Keywords:

Unified AI model, Cloud security, Financial fraud prevention, Medical image intelligence, 5G web applications, Deep learning, Real-time analytics, Edge computing, Data privacy, Secure cloud architecture

Abstract

The rapid integration of artificial intelligence (AI), cloud computing, and 5G networking is enabling transformative solutions across industries. This study proposes a unified AI and cloud security model that combines financial fraud prevention and medical image intelligence within 5G-powered web applications. The framework leverages cloud-native security features and AI-driven analytics to detect, prevent, and respond to threats in real time. In financial systems, machine learning models analyze transaction patterns, user behavior, and network anomalies to identify fraudulent activities, reducing financial losses and improving trust. In healthcare, deep learning algorithms interpret medical images such as X-rays, CT scans, and MRIs, enabling faster and more accurate diagnoses. The 5G-enabled web application layer ensures high-speed data transfer, low latency, and reliable access for mobile and IoT devices, supporting telemedicine and remote financial services. The unified model emphasizes secure data storage, end-to-end encryption, identity management, and compliance with privacy regulations. By integrating AI, cloud security, and 5G technologies, the proposed model provides a scalable, interoperable, and resilient platform that enhances fraud prevention and medical intelligence. The framework also supports real-time monitoring and automated response mechanisms, contributing to safer and more efficient digital ecosystems.

References

1. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.

2. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.

3. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.

4. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.

5. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.

6. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.

7. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).

8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

9. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

10. Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining (pp. 413–422).

11. Kasireddy, J. R. (2022). From raw trades to audit-ready insights: Designing regulator-grade market surveillance pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4609–4616. https://doi.org/10.15662/IJEETR.2022.0402003

12. Madabathula, L. (2022). Event-driven BI pipelines for operational intelligence in Industry 4.0. International Journal of Research and Applied Innovations (IJRAI), 5(2), 6759–6769. https://doi.org/10.15662/IJRAI.2022.0502005

13. Chivukula, V. (2021). Impact of Bias in Incrementality Measurement Created on Account of Competing Ads in Auction Based Digital Ad Delivery Platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4345–4350.

14. Russakovsky, O., Deng, J., Su, H., et al. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211–252.

15. Singh, A. (2021). Mitigating DDoS attacks in cloud networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(4), 3386–3392. https://doi.org/10.15662/IJEETR.2021.0304003

16. S. M. Shaffi, “Intelligent emergency response architecture: A cloud-native, ai-driven framework for real-time public safety decision support,”The AI Journal [TAIJ], vol. 1, no. 1, 2020.

17. Sze, V., Chen, Y. H., Yang, T. J., & Emer, J. S. (2020). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 108(11), 1935–1967.

18. Kesavan, E. (2022). Driven learning and collaborative automation innovation via Trailhead and Tosca user groups. International Scientific Journal of Engineering and Management, 1(1), Article 00058. https://doi.org/10.55041/ISJEM00058

19. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.

20. Kumar, S. N. P. (2022). Text Classification: A Comprehensive Survey of Methods, Applications, and Future Directions. International Journal of Technology, Management and Humanities, 8(3), 39–49. https://ijtmh.com/index.php/ijtmh/article/view/227/222

21. Kumar, R., & Panda, M. R. (2022). Benchmarking Hallucination Detection in LLMs for Regulatory Applications Using SelfCheckGPT. Journal of Artificial Intelligence & Machine Learning Studies, 6, 149-181.

22. Zhang, C., & Ma, Y. (2012). Ensemble machine learning: Methods and applications. Springer.