An Intelligent Fraud Prevention Framework with Deep Learning, Cloud-Native DevSecOps CI/CD, SAP HANA ERP Analytics, and LLM-Based Declarative Reasoning
DOI:
https://doi.org/10.15662/IJARCST.2021.0405006Keywords:
Fraud Detection, Deep Learning, SAP HANA, Cloud-Native Architecture, DevSecOps, CI/CD, ERP Analytics, LLM Reasoning, Declarative Reasoning, Cybersecurity, Autoencoders, Anomaly Detection, In-Memory Databases, Secure Pipelines, AI GovernanceAbstract
Modern enterprises face unprecedented fraud risks due to digital expansion, complex supply chains, remote-access infrastructures, and large-scale cloud integrations. Traditional rule-based fraud detection systems are no longer sufficient for high-velocity and high-dimensional transactional environments. This research proposes an Intelligent Fraud Prevention Framework that unifies deep learning, cloud-native DevSecOps CI/CD automation, SAP HANA in-memory ERP analytics, and LLM-based declarative reasoning. The integrated architecture enables real-time anomaly detection, secure pipeline operations, continuous compliance, and interpretable decision workflows. Deep neural networks and autoencoders detect subtle financial and operational anomalies, while SAP HANA accelerates transactional analytics and contextual feature engineering. Cloud-native DevSecOps ensures automated vulnerability scanning, policy enforcement, model versioning, and deployment security. Large Language Models (LLMs) provide declarative reasoning, explainability, and intelligent query interfaces enabling auditors and risk officers to articulate complex fraud patterns using natural language. Experimental evaluation demonstrates improved detection precision, reduced false positives, and enhanced operational resilience. The framework contributes a scalable, explainable, and secure fraud-mitigation infrastructure suitable for banking, e-commerce, government, and ERP-driven enterprises. Future directions include federated learning, zero-trust authentication, autonomous threat simulations, and governance-driven LLM regulatory compliance.References
1. Aggarwal, C. (2015). Outlier Analysis. Springer.
2. 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.
3. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
4. Bahnsen, A., Aouada, D., & Ottersten, B. (2016). Example-dependent cost-sensitive decision trees. Engineering Applications of AI.
5. Arora, Anuj. "The Significance and Role of AI in Improving Cloud Security Posture for Modern Enterprises." International Journal of Current Engineering and Scientific Research (IJCESR), vol. 5, no. 5, 2018, ISSN 2393-8374 (Print), 2394-0697 (Online).
6. Konidena, B. K., Bairi, A. R., & Pichaimani, T. (2021). Reinforcement Learning-Driven Adaptive Test Case Generation in Agile Development. American Journal of Data Science and Artificial Intelligence Innovations, 1, 241-273.
7. Vijayaboopathy, V., Ananthakrishnan, V., & Mohammed, A. S. (2020). Transformer-Based Auto-Tuner for PL/SQL and Shell Scripts. Journal of Artificial Intelligence & Machine Learning Studies, 4, 39-70.
8. Navandar, Pavan. "Enhancing Cybersecurity in Airline Operations through ERP Integration: A Comprehensive Approach." Journal of Scientific and Engineering Research 5, no. 4 (2018): 457-462.
9. 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.
10. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
11. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
12. Guo, T., & Li, H. (2018). Fraud detection with graph neural networks. IEEE TKDE.
13. Hochreiter, S., & Schmidhuber, J. (2006). LSTM. Neural Computation.
14. Kim, K., & Kim, J. (2015). DevSecOps security automation. Journal of Information Security.
15. Kogan, A., Alles, M., & Vasarhelyi, M. (2014). Continuous auditing. Auditing Journal.
16. Kumar, V., & Chhabra, A. (2019). Autoencoder-based anomaly detection. Neurocomputing.
17. Liu, F. (2017). Cloud computing security guidelines. NIST Special Publication.
18. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132-151.
19. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
20. 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.
21. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
22. Hardial Singh, “ENHANCING CLOUD SECURITY POSTURE WITH AI-DRIVEN THREAT DETECTION AND RESPONSE MECHANISMS”, INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR), VOLUME-6, ISSUE-2, 2019.
23. Zhang, Y., & Chen, Z. (2020). Neural approaches for fraud detection. IEEE Access.
