A Unified AI LLM and Cloud Security Model for Financial Fraud Detection and ETL-Based Data Integration in Web Systems
Abstract
This research introduces a unified system that integrates large language models (LLMs) and cloud security mechanisms within web systems to improve financial fraud detection and ETL-based data integration. The proliferation of digital financial transactions and complex data pipelines necessitates intelligent approaches that can both analyze textual/transactional anomalies and secure sensitive data flows. We propose a hybrid architecture that combines advanced LLMs trained on financial datasets with cloud security services, enabling real-time fraud identification through natural language and pattern recognition, while simultaneously safeguarding data during extraction, transformation, and loading (ETL) processes. This model leverages scalable cloud platforms (e.g., serverless functions, container orchestration) and secure key management to ensure compliance with privacy standards and enhance resilience against threats. Experimental evaluations on benchmark datasets demonstrate significant improvements in fraud detection accuracy and latency compared to traditional rule-based systems. The unified model also achieves robust data integration performance with low error rates and high throughput. Findings suggest that incorporating AI LLM capabilities within secure cloud infrastructures can empower organizations to detect sophisticated threats and maintain integrity across distributed web systems. We discuss design choices, implementation challenges, and implications for future intelligent financial systems.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
15265-15273 |
Published |
November 11, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Laura Emilia Virtanen (2024). A Unified AI LLM and Cloud Security Model for Financial Fraud Detection and ETL-Based Data Integration in Web Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 6 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 15265-15273. https://doi.org/10.15662/IJAESIT.2024.0706002 |
References
2. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.
3. Kumar, S. S. (2024). Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems. International Journal of Humanities and Information Technology, 6(04), 54-59.
4. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review. Decision Support Systems, 50(3), 559–569.
5. 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
6. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
7. Panda, M. R., Selvaraj, A., & Muthusamy, P. (2023). FinTech Trading Surveillance Using LLM-Powered Anomaly Detection with Isolation Forests. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 530-564.
8. Kubam, C. S. (2026). Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines. arXiv preprint arXiv:2601.06241.
9. Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
10. Kimball, R., & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley.
11. Kaufman, C. (2009). Network Security: Private Communication in a Public World (2nd ed.). Prentice Hall.
12. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188.
13. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
14. Chivukula, V. (2023). Calibrating Marketing Mix Models (MMMs) with Incrementality Tests. International Journal of Research and Applied Innovations (IJRAI), 6(5), 9534–9538.
15. Vasugi, T. (2023). Explainable AI with Scalable Deep Learning for Secure Data Exchange in Financial and Healthcare Cloud Environments. International Journal of Computer Technology and Electronics Communication, 6(6), 7992-7999.
16. Natta, P. K. (2023). Robust supply chain systems in cloud-distributed environments: Design patterns and insights. International Journal of Research and Applied Innovations (IJRAI), 6(4), 9222–9231. https://doi.org/10.15662/IJRAI.2023.0604006
17. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.
18. Kasireddy, J. R. (2023). Optimizing multi-TB market data workloads: Advanced partitioning and skew mitigation strategies for Hive and Spark on EMR. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 6982–6990. https://doi.org/10.15680/IJCTECE.2023.0603005
19. Madabathula, L. (2023). Scalable risk-aware ETL pipelines for enterprise subledger analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(6), 9737–9745. https://doi.org/10.15662/IJRPETM.2023.0606015
20. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
21. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.
22. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems, 30, 5998–6008.
23. Sarker, I. H. (2021). Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Computer Science, 2(6), 420.