Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection
DOI:
https://doi.org/10.15662/IJARCST.2024.0701004Keywords:
SAP, Financial Compliance, Audit Automation, Anomaly Detection, Machine Learning, Federated Learning, Explainable AI, Cloud Integration, ERP, Continuous Assurance.Abstract
In today’s highly regulated financial landscape, enterprises increasingly rely on SAP (Systems, Applications, and Products) ERP environments to manage mission-critical financial operations. However, the scale, volume, and complexity of financial transactions in SAP systems pose substantial challenges for audit and compliance teams, especially when trying to identify anomalies, regulatory breaches, or fraud efficiently. This paper explores cloud-integrated artificial intelligence (AI) models tailored to enhance financial compliance and audit processes within SAP ecosystems. Specifically, we examine how machine learning (ML), anomaly detection, self-supervised learning, and federated learning can be embedded in cloud-connected SAP modules to provide continuous, intelligent monitoring of financial data. We conduct a comprehensive literature review of contemporary techniques, including journal-entry anomaly detection, contrastive self-supervised learning, explainable AI for audits, and adversarial learning. Our research methodology combines threat modeling, performance simulation, and a design framework for cloud-native AI-assist audit architecture, leveraging SAP Business AI, governance frameworks, and federated learning. We evaluate the proposed architecture along dimensions of accuracy, scalability, interpretability, data privacy, and audit-readiness. The results show that integrating AI-driven anomaly detection with SAP’s cloud components can significantly reduce false positives and elevate the efficiency of audit sampling, while preserving data confidentiality through privacy-aware techniques. However, challenges remain around model explainability, risk of adversarial manipulation, and governance of AI in regulated financial environments. Our discussion outlines trade-offs, design guidelines, and compliance best practices for deploying such systems. In conclusion, a cloud-integrated AI-audit framework holds strong promise for financial institutions leveraging SAP, offering proactive risk detection, continuous assurance, and enhanced regulatory resilience. We also identify future directions such as federated continual learning, robust adversarial-defense models, and tighter integration with SAP GRC (Governance, Risk & Compliance) modules.
References
1. Bakumenko, A., & et al. (2022). Detecting anomalies in financial data using machine learning. Systems, 10(5), 130. MDPI
2. Ali, A., & et al. (2022). Financial Fraud Detection Based on Machine Learning. Applied Sciences, 12(19), 9637. MDPI
3. Kumar, R. K. (2022). AI-driven secure cloud workspaces for strengthening coordination and safety compliance in distributed project teams. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8075–8084. https://doi.org/10.15662/IJRAI.2022.0506017
4. Karanjkar, R. (2022). Resiliency Testing in Cloud Infrastructure for Distributed Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7142-7144.
5. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
6. Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology, 6(2), 7941–7950. https://doi.org/0.15662/IJARCST.2023.0602004
7. Thangavelu, K., Muthirevula, G. R., & Mallareddi, P. K. D. (2023). Kubernetes Migration in Regulated Industries: Transitioning from VMware Tanzu to Azure Kubernetes Service (AKS). Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 35-76.
8. Selvi, R., Saravan Kumar, S., & Suresh, A. (2014). An intelligent intrusion detection system using average manhattan distance-based decision tree. In Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 1 (pp. 205-212). New Delhi: Springer India.
9. Konda, S. K. (2022). STRATEGIC EXECUTION OF SYSTEM-WIDE BMS UPGRADES IN PEDIATRIC HEALTHCARE ENVIRONMENTS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7123-7129.
10. Kumar, S. N. P. (2022). Improving Fraud Detection in Credit Card Transactions Using Autoencoders and Deep Neural Networks (Doctoral dissertation, The George Washington University).
11. Mohile, A. (2023). Next-Generation Firewalls: A Performance-Driven Approach to Contextual Threat Prevention. International Journal of Computer Technology and Electronics Communication, 6(1), 6339-6346.
12. Schreyer, M., Sattarov, T., & Borth, D. (2021). Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks. arXiv. arXiv
13. Joseph, J. (2023). Trust, but Verify: Audit-ready logging for clinical AI. https://www.researchgate.net/profile/JimmyJoseph9/publication/395305525_Trust_but_Verify_Audit -ready_logging_for_clinical_AI/links/68bbc5046f87c42f3b9011db/Trust-but-Verify-Audit-readylogging-for-clinical-AI.pdf
14. Müller, R., Schreyer, M., Sattarov, T., & Borth, D. (2022). RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. ACM Digital Library
15. Peram, S. (2023). Machine Learning in Wealth Management: Enhancing Investment Strategies through AI. https://www.researchgate.net/profile/Sudhakara-Peram/publication/396293166_Machine_Learning_in_Wealth_Management_Enhancing_Investment_Strategies_through_AI/links/68e5f128ffdca73694b6174e/Machine-Learning-in-Wealth-Management-Enhancing-Investment-Strategies-through-AI.pdf
16. Christadoss, J., Kalyanasundaram, P. D., & Vunnam, N. (2024). Hybrid GraphQL-FHIR Gateway for Real-Time Retail-Health Data Interchange. Essex Journal of AI Ethics and Responsible Innovation, 4, 204-238.
17. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
18. Pasumarthi, A. (2023). Dynamic Repurpose Architecture for SAP Hana Transforming DR Systems into Active Quality Environments without Compromising Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6263-6274.
19. 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.
20. Chatterjee, P. (2019). Enterprise Data Lakes for Credit Risk Analytics: An Intelligent Framework for Financial Institutions. Asian Journal of Computer Science Engineering, 4(3), 1-12. https://www.researchgate.net/profile/Pushpalika-Chatterjee/publication/397496748_Enterprise_Data_Lakes_for_Credit_Risk_Analytics_An_Intelligent_Framework_for_Financial_Institutions/links/69133ebec900be105cc0ce55/Enterprise-Data-Lakes-for-Credit-Risk-Analytics-An-Intelligent-Framework-for-Financial-Institutions.pdf
21. Kotapati, V. B. R., Perumalsamy, J., & Yakkanti, B. (2022). Risk-Adapted Investment Strategies using Quantum-enhanced Machine Learning Models. American Journal of Autonomous Systems and Robotics Engineering, 2, 279-312.
22. 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.
23. 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.
24. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
